{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2eec5cc39a59"
      },
      "outputs": [],
      "source": [
        "# Copyright 2024 Google LLC\n",
        "#\n",
        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "#     https://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JAPoU8Sm5E6e"
      },
      "source": [
        "# Anomaly Detection of Infrastructure Logs using Gemini and BigQuery Vector Search\n",
        "\n",
        "<table align=\"left\">\n",
        "  <td style=\"text-align: center\">\n",
        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/generative-ai/blob/main/embeddings/use-cases/outlier-detection/bq-vector-search-outlier-detection-infra-logs.ipynb\">\n",
        "      <img width=\"32px\" src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Google Colaboratory logo\"><br> Open in Colab\n",
        "    </a>\n",
        "  </td>\n",
        "  <td style=\"text-align: center\">\n",
        "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fgenerative-ai%2Fmain%2Fembeddings%2Fuse-cases%2Foutlier-detection%2Fbq-vector-search-outlier-detection-infra-logs.ipynb\">\n",
        "      <img width=\"32px\" src=\"https://lh3.googleusercontent.com/JmcxdQi-qOpctIvWKgPtrzZdJJK-J3sWE1RsfjZNwshCFgE_9fULcNpuXYTilIR2hjwN\" alt=\"Google Cloud Colab Enterprise logo\"><br> Open in Colab Enterprise\n",
        "    </a>\n",
        "  </td>\n",
        "  <td style=\"text-align: center\">\n",
        "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/generative-ai/main/embeddings/use-cases/outlier-detection/bq-vector-search-outlier-detection-infra-logs.ipynb\">\n",
        "      <img src=\"https://www.gstatic.com/images/branding/gcpiconscolors/vertexai/v1/32px.svg\" alt=\"Vertex AI logo\"><br> Open in Vertex AI Workbench\n",
        "    </a>\n",
        "  </td>\n",
        "  <td style=\"text-align: center\">\n",
        "    <a href=\"https://console.cloud.google.com/bigquery/import?url=https://github.com/GoogleCloudPlatform/generative-ai/blob/main/embeddings/use-cases/outlier-detection/bq-vector-search-outlier-detection-infra-logs.ipynb\">\n",
        "      <img src=\"https://www.gstatic.com/images/branding/gcpiconscolors/bigquery/v1/32px.svg\" alt=\"BigQuery Studio logo\"><br> Open in BigQuery Studio\n",
        "    </a>\n",
        "  </td>\n",
        "  <td style=\"text-align: center\">\n",
        "    <a href=\"https://github.com/GoogleCloudPlatform/generative-ai/blob/main/embeddings/use-cases/outlier-detection/bq-vector-search-outlier-detection-infra-logs.ipynb\">\n",
        "      <img width=\"32px\" src=\"https://raw.githubusercontent.com/primer/octicons/refs/heads/main/icons/mark-github-24.svg\" alt=\"GitHub logo\"><br> View on GitHub\n",
        "    </a>\n",
        "  </td>\n",
        "</table>\n",
        "\n",
        "<div style=\"clear: both;\"></div>\n",
        "\n",
        "<b>Share to:</b>\n",
        "\n",
        "<a href=\"https://www.linkedin.com/sharing/share-offsite/?url=https%3A//github.com/GoogleCloudPlatform/generative-ai/blob/main/embeddings/use-cases/outlier-detection/bq-vector-search-outlier-detection-infra-logs.ipynb\" target=\"_blank\">\n",
        "  <img width=\"20px\" src=\"https://upload.wikimedia.org/wikipedia/commons/8/81/LinkedIn_icon.svg\" alt=\"LinkedIn logo\">\n",
        "</a>\n",
        "\n",
        "<a href=\"https://bsky.app/intent/compose?text=https%3A//github.com/GoogleCloudPlatform/generative-ai/blob/main/embeddings/use-cases/outlier-detection/bq-vector-search-outlier-detection-infra-logs.ipynb\" target=\"_blank\">\n",
        "  <img width=\"20px\" src=\"https://upload.wikimedia.org/wikipedia/commons/7/7a/Bluesky_Logo.svg\" alt=\"Bluesky logo\">\n",
        "</a>\n",
        "\n",
        "<a href=\"https://twitter.com/intent/tweet?url=https%3A//github.com/GoogleCloudPlatform/generative-ai/blob/main/embeddings/use-cases/outlier-detection/bq-vector-search-outlier-detection-infra-logs.ipynb\" target=\"_blank\">\n",
        "  <img width=\"20px\" src=\"https://upload.wikimedia.org/wikipedia/commons/5/5a/X_icon_2.svg\" alt=\"X logo\">\n",
        "</a>\n",
        "\n",
        "<a href=\"https://reddit.com/submit?url=https%3A//github.com/GoogleCloudPlatform/generative-ai/blob/main/embeddings/use-cases/outlier-detection/bq-vector-search-outlier-detection-infra-logs.ipynb\" target=\"_blank\">\n",
        "  <img width=\"20px\" src=\"https://redditinc.com/hubfs/Reddit%20Inc/Brand/Reddit_Logo.png\" alt=\"Reddit logo\">\n",
        "</a>\n",
        "\n",
        "<a href=\"https://www.facebook.com/sharer/sharer.php?u=https%3A//github.com/GoogleCloudPlatform/generative-ai/blob/main/embeddings/use-cases/outlier-detection/bq-vector-search-outlier-detection-infra-logs.ipynb\" target=\"_blank\">\n",
        "  <img width=\"20px\" src=\"https://upload.wikimedia.org/wikipedia/commons/5/51/Facebook_f_logo_%282019%29.svg\" alt=\"Facebook logo\">\n",
        "</a>            "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "84f0f73a0f76"
      },
      "source": [
        "| | |\n",
        "|-|-|\n",
        "| Author: | [Roy Arsan](https://github.com/rarsan) |"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tvgnzT1CKxrO"
      },
      "source": [
        "## Overview\n",
        "\n",
        "This notebook guides you through processing large volumes of logs using Gemini, vector embeddings and [BigQuery Vector Search](https://cloud.google.com/bigquery/docs/vector-search-intro) to perform anomaly detection. We evaluate the accuracy using publicly available labelled [HDFS](https://github.com/logpai/loghub) dataset, and contrast it with other common log anomaly detection techniques.\n",
        "\n",
        "\n",
        "As part of this notebook you will learn how to:\n",
        "1. Use SQL for preprocessing raw logs at scale, interact with LLMs and do vector search analysis\n",
        "1. Use Gemini 2.0 to translate a log sequence into simple natural language summary directly from BigQuery\n",
        "1. Use a text embedding model to generate a vector embedding for each log summary directly from BigQuery, and store all embeddings in BigQuery vector index for fast lookup\n",
        "1. Tune and run vector search-based anomaly detection using BigQuery vector search\n",
        "1. Evaluate performance and compare against popular unsupervised and semi-supervised outlier detection ML algorithms\n",
        "\n",
        "\n",
        "This notebook demonstrates how using off-the-shelf Gemini 2.0 + Text Embeddings + BigQuery vector search yields comparable results to custom pre-trained language models including DeepLog, an [LSTM deep neural network](https://dl.acm.org/doi/10.1145/3133956.3134015).\n",
        "\n",
        "We also compare vector search-based outlier detection with common [scikit outlier detection](https://scikit-learn.org/stable/modules/outlier_detection.html#) algorithms (OneClassSVM, LocalOutlierFactor) using the same generated embeddings and we found that a custom SQL logic using BigQuery vector search to be more accurate and flexible for this use case and dataset.\n",
        "\n",
        "For performance results, see `Summary` section at the bottom.\n",
        "\n",
        " You can use and tweak this notebook to evaluate for your own outlier detection use case, with your own dataset and target sensitivity level."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DW8bwUaS2JQj"
      },
      "source": [
        "### Data pipeline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rt10T8iZrNSc"
      },
      "source": [
        "![anomaly-detection-data-pipeline.png](https://storage.googleapis.com/github-repo/generative-ai/embeddings/use-cases/outlier-detection/bq-vector-search-outlier-detection-infra-logs/anomaly-detection-data-pipeline.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sh4i937UcI2q"
      },
      "source": [
        "### Task, metrics and dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YWviBrbU7cJ_"
      },
      "source": [
        "Outlier detection, specifically novelty detection, is about detecting all anomalies including 'unknown' types of anomalies (e.g. system failures, new cyberattacks, etc.). Therefore supervised approaches where you train the model on both normal and abnormal logs are simply not applicable. Therefore, the approach described in this notebook is compared against leading and popular unsupervised and semi-supervised techniques. **Recall** is our primary objective for outlier detection (minimize false negatives) while also keeping reasonably high **precision** (minimize false positives).\n",
        "\n",
        "We use the open-source labelled HDFS dataset from [Loghub]((https://github.com/logpai/loghub) which is freely accessible and commonly used in log analysis evaluations, including anomaly detection. HDFS dataset provides 11M+ system log lines (\\~575k log sessions) collected from a real Hadoop cluster on 200 nodes, and labelled by Hadoop domain experts to identify the runtime anomalies. For the purpose of this notebook, we use a subset of the HDFS dataset (70k log sessions or ~12%) taking into consideration default BigQuery ML quota limits (e.g. 200 rpm and 72,000 rows per job with `gemini-2.0-flash` at the time of this writing). This way you can run this notebook as is in your own Google Cloud project in a timely fashion without necessarily requesting quota increases."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "61RBz8LLbxCR"
      },
      "source": [
        "## Getting started"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "No17Cw5hgx12"
      },
      "source": [
        "### Install Vertex AI SDK and other required packages\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tFy3H3aPgx12"
      },
      "outputs": [],
      "source": [
        "%pip install --upgrade --user --quiet \\\n",
        "    google-cloud-aiplatform \\\n",
        "    google-cloud-bigquery \\\n",
        "    scikit-learn==1.3.1"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R5Xep4W9lq-Z"
      },
      "source": [
        "### Restart runtime\n",
        "\n",
        "To use the newly installed packages in this Jupyter runtime, you must restart the runtime. You can do this by running the cell below, which restarts the current kernel.\n",
        "\n",
        "The restart might take a minute or longer. After it's restarted, continue to the next step."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XRvKdaPDTznN"
      },
      "outputs": [],
      "source": [
        "import IPython\n",
        "\n",
        "app = IPython.Application.instance()\n",
        "app.kernel.do_shutdown(True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SbmM4z7FOBpM"
      },
      "source": [
        "<div class=\"alert alert-block alert-warning\">\n",
        "<b>⚠️ The kernel is going to restart. Wait until it's finished before continuing to the next step. ⚠️</b>\n",
        "</div>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dmWOrTJ3gx13"
      },
      "source": [
        "### Authenticate your notebook environment (Colab only)\n",
        "\n",
        "If you're running this notebook on Google Colab, run the cell below to authenticate your environment."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NyKGtVQjgx13"
      },
      "outputs": [],
      "source": [
        "import sys\n",
        "\n",
        "if \"google.colab\" in sys.modules:\n",
        "    from google.colab import auth\n",
        "\n",
        "    auth.authenticate_user()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DF4l8DTdWgPY"
      },
      "source": [
        "### Set Google Cloud project information and initialize Vertex AI SDK\n",
        "\n",
        "To get started using Vertex AI, you must have an existing Google Cloud project and [enable the Vertex AI API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com).\n",
        "\n",
        "Learn more about [setting up a project and a development environment](https://cloud.google.com/vertex-ai/docs/start/cloud-environment)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Nqwi-5ufWp_B"
      },
      "outputs": [],
      "source": [
        "PROJECT_ID = \"[project-id]\"  # @param {type:\"string\"}\n",
        "REGION = \"us-central1\"  # @param {type:\"string\"}\n",
        "LOCATION = \"US\"  # @param {type:\"string\"}\n",
        "\n",
        "import vertexai\n",
        "\n",
        "vertexai.init(project=PROJECT_ID, location=REGION)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5303c05f7aa6"
      },
      "source": [
        "### Import libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "01qn1OZeh_EP"
      },
      "outputs": [],
      "source": [
        "import bigframes.pandas as bpd\n",
        "from google.cloud import bigquery\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Import necessary libraries\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "from sklearn.manifold import TSNE"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jLc02A9ch_EP"
      },
      "outputs": [],
      "source": [
        "# initialize bigquery client\n",
        "client = bigquery.Client()\n",
        "\n",
        "# initialize bigquery dataframes client\n",
        "bpd.close_session()\n",
        "bpd.options.bigquery.project = PROJECT_ID\n",
        "bpd.options.bigquery.location = LOCATION"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_68282UqlxrR"
      },
      "outputs": [],
      "source": [
        "DATASET = \"vs_logs_demo\"  # @param {type:\"string\"}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aF5qTVGRh_EP"
      },
      "source": [
        "### Create a new dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "L6OQ55Axh_EP"
      },
      "outputs": [],
      "source": [
        "%%bigquery\n",
        "CREATE SCHEMA `vs_logs_demo` OPTIONS (LOCATION = 'US');"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AxdBl98uUM09"
      },
      "source": [
        "### Create Cloud resource connection for Vertex AI"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qsJ_dwvG-28r"
      },
      "source": [
        "You need to create a Cloud resource connection to enable BigQuery to interact with Vertex AI services such as LLM remote models."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7EZlKHyyUMNY"
      },
      "outputs": [],
      "source": [
        "CONNECTION_NAME = \"bq-llm-connection\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BVbxqd9C_Tki"
      },
      "outputs": [],
      "source": [
        "!bq mk --connection --location={LOCATION} --project_id={PROJECT_ID} \\\n",
        "    --connection_type=CLOUD_RESOURCE {CONNECTION_NAME}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pQphx8hz_cyj"
      },
      "source": [
        "Retrieve the connection service account ID"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5hgJatLm_eFF"
      },
      "outputs": [],
      "source": [
        "!bq show --connection {PROJECT_ID}.{LOCATION}.{CLOUD_RESOURCE_CONN}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1YVxe9B3_hhe"
      },
      "source": [
        "Copy the service account ID in the following parameter:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XPqGhAt6_jUU"
      },
      "outputs": [],
      "source": [
        "# Copy the value of `serviceAccountId` field in last cell's output\n",
        "CONNECTION_SA_ID = \"[bq-connection-service-account]\"  # @param {type:\"string\"}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wlsjW4-X_vOw"
      },
      "source": [
        "Give that service account access to Vertex AI"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oKYvvcZM_wMu"
      },
      "outputs": [],
      "source": [
        "!gcloud projects add-iam-policy-binding {PROJECT_ID} --member='serviceAccount:{CONNECTION_SA_ID}' --role='roles/aiplatform.user' --condition=None --no-user-output-enabled"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E0qF8NR7_0o3"
      },
      "source": [
        "If your notebook environment does not have `setIamPolicy` permission on the project, you (or your project's administrator) can alternatively grant  permissions to that cloud connection's service account using Cloud Console following these steps: https://cloud.google.com/bigquery/docs/generate-text-tutorial-gemini#grant-permissions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HysosuDkh_Ec"
      },
      "source": [
        "## Import logs dataset into BigQuery"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iVPIbKUzXx5l"
      },
      "source": [
        "The HDFS dataset was originally developed by Wei Xu et al. in [Detecting Large-Scale System Problems by Mining Console Logs](https://people.eecs.berkeley.edu/~jordan/papers/xu-etal-sosp09.pdf) and made available in [Wei Xu's website](https://people.iiis.tsinghua.edu.cn/~weixu/sospdata.html). The raw logs are also available at [loghub](https://github.com/logpai/loghub) thanks to the work of Zhu et al. in [Loghub: A Large Collection of System Log Datasets for AI-driven Log Analytics](https://arxiv.org/abs/2008.06448). To facilitate log ingestion and analysis in this notebook, the logs are expected to be converted to structured format (csv) using a standard log parser such as [this one](https://github.com/logpai/logparser/blob/main/logparser/Drain/README.md) from [logparser](https://github.com/logpai/logparser).\n",
        "\n",
        "For the purpose of this notebook, the pre-parsed logs are available as .csv files in a GCS bucket:\n",
        "\n",
        "*   Full dataset: `HDFS.log_structured.csv` (2.9 GB)\n",
        "*   Limited 2k dataset: `HDFS_2k.log_structured.csv` (< 1 MB)\n",
        "\n",
        "You will import the full dataset in this notebook, but subsequently process ~15% of the data."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aRBDEawbh_Ef"
      },
      "source": [
        "### Load logs from GCS"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2zn4ds3gh_Ef"
      },
      "outputs": [],
      "source": [
        "gcs_bucket = \"gs://csa-datasets-public/hdfs/\"\n",
        "gcs_filename = \"HDFS.log_structured.csv\"  # @param {type:\"string\"}\n",
        "\n",
        "gcs_uri = gcs_bucket + gcs_filename\n",
        "\n",
        "\n",
        "def camelCase(col):\n",
        "    return col[0].lower() + col[1:]\n",
        "\n",
        "\n",
        "df = bpd.read_csv(gcs_uri, engine=\"bigquery\")\n",
        "# lowercase first letter of each column name\n",
        "df.columns = [camelCase(col) for col in df.columns]\n",
        "print(df.info())\n",
        "\n",
        "df.to_gbq(\"vs_logs_demo.hdfs_full_structured\", if_exists=\"replace\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CUseo2PAh_Ef"
      },
      "source": [
        "### Group logs by sessions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mgfkFhITXVm-"
      },
      "source": [
        "Grouping logs by session ID or other unique identifiers allows us to correlate events that belong to the same user or transaction, providing context and a clear chronological sequence of actions. This is essential for understanding user behavior, troubleshooting issues, and identifying patterns that might otherwise be lost in the noise of a massive, undifferentiated log stream.\n",
        "\n",
        "In our case, since HDFS logs are records of events related to file system operations at the block level, such as block replication and deletion, we need to group HDFS logs by unique `blockId`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "D-fsGL0Uh_Eg"
      },
      "outputs": [],
      "source": [
        "%%bigquery\n",
        "CREATE OR REPLACE TABLE `vs_logs_demo.hdfs_full_sessionized` AS (\n",
        "  SELECT\n",
        "    blockId,\n",
        "    -- Capture start and end time\n",
        "    MIN(timestamp) AS ts_start,\n",
        "    MAX(timestamp) AS ts_end,\n",
        "    COUNT(*) AS numEvents,\n",
        "    -- Concatenate sequence of events\n",
        "    STRING_AGG(CONCAT(\n",
        "     component, \" \", level, \" \", content\n",
        "    ), '\\n' ORDER BY timestamp ASC) AS eventSequence,\n",
        "    -- Concatenate sequence of event templates\n",
        "    STRING_AGG(CONCAT(\n",
        "     component, \" \", level, \" \", eventTemplate\n",
        "    ), '\\n' ORDER BY timestamp ASC) AS eventTemplateSequence,\n",
        "    -- For debug only\n",
        "    ARRAY_AGG(STRUCT(\n",
        "        timestamp, level, component, content, eventId, eventTemplate\n",
        "    ) ORDER BY timestamp ASC) AS sequence,\n",
        "  FROM (\n",
        "    SELECT\n",
        "      PARSE_TIMESTAMP('%y%m%d %H%M%S', CONCAT(LPAD(CAST(date AS STRING), 6, '0'), ' ', LPAD(CAST(time AS STRING), 6, '0'))) AS timestamp,\n",
        "      REGEXP_EXTRACT(content, r'(blk_-?[0-9]+)') AS blockId,\n",
        "      * EXCEPT(date, time)\n",
        "    FROM `vs_logs_demo.hdfs_full_structured`\n",
        "  )\n",
        "  GROUP BY\n",
        "    blockId\n",
        "  ORDER BY\n",
        "    ts_start ASC, blockId ASC\n",
        ");"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DhANs7B7h_Eg"
      },
      "source": [
        "### Label log sequences"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xjRjHbkLawlP"
      },
      "source": [
        "This is a labelled dataset where each `blockId` has a binary label: either 'Normal' or 'Anomaly'. Let's retrieve the labels CSV file and store it in separate BigQuery table `hdfs_labels`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sbTbkLv5h_Eg"
      },
      "outputs": [],
      "source": [
        "gcs_uri = \"gs://csa-datasets-public/hdfs/anomaly_label.csv\"\n",
        "df_labels = bpd.read_csv(gcs_uri, engine=\"bigquery\", header=0)\n",
        "df_labels.columns = [\"blockId\", \"label\"]\n",
        "print(df_labels.info())\n",
        "\n",
        "df_labels.to_gbq(\"vs_logs_demo.hdfs_labels\", if_exists=\"replace\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ADUU9kcJh_Eg"
      },
      "source": [
        "Let's correlate the log sequences with the labels table, and create a new labelled table for log sequences by setting a new boolean column `abnormal`. We also capture the sequential order of rows in `sessionId` which will come in handy later on for splitting the dataset and batching calls to Vertex AI API given its rate limits."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iFmql46fh_Eh"
      },
      "outputs": [],
      "source": [
        "%%bigquery\n",
        "CREATE OR REPLACE TABLE `vs_logs_demo.hdfs_full_labelled` AS (\n",
        "SELECT\n",
        "  -- Capture sequential order of sessions\n",
        "  ROW_NUMBER() OVER(ORDER BY logs.ts_start ASC) AS sessionId,\n",
        "  logs.* EXCEPT(sequence),\n",
        "  CASE\n",
        "    WHEN labels.label = 'Anomaly' THEN TRUE\n",
        "    ELSE FALSE\n",
        "  END AS abnormal\n",
        "FROM `vs_logs_demo.hdfs_full_sessionized` logs\n",
        "LEFT JOIN `vs_logs_demo.hdfs_labels` labels USING (blockId)\n",
        ")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A52AUFlzh_Eh"
      },
      "source": [
        "### Exploratory data analysis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TgKum7IZh_Eh"
      },
      "outputs": [],
      "source": [
        "df = bpd.read_gbq(\"vs_logs_demo.hdfs_full_labelled\")\n",
        "df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nUtFdr5lh_Eh"
      },
      "outputs": [],
      "source": [
        "df.head(5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XwpnSSbjh_Eh"
      },
      "source": [
        "Let's see the distribution of log sequence length, by displaying summary stats for `numEvents` numeric column"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VZq7oaj9h_Ei"
      },
      "outputs": [],
      "source": [
        "df.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OYm6XxT6VI1e"
      },
      "source": [
        "Let's the sample the data and get a breakdown of lengths of log sequences"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RxNtzI0Ph_Ei"
      },
      "outputs": [],
      "source": [
        "bpd.options.sampling.enable_downsampling = True  # enable downsampling\n",
        "bpd.options.sampling.max_download_size = 200  # download only 200 mb of data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dQ-ODwuHh_Ei"
      },
      "outputs": [],
      "source": [
        "local_df = df.to_pandas(\n",
        "    max_download_size=200, sampling_method=\"uniform\", random_state=101\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9yrQLumPh_Ej"
      },
      "outputs": [],
      "source": [
        "sns.countplot(x=\"abnormal\", data=local_df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9u6ADdKwh_Ej"
      },
      "outputs": [],
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "\n",
        "# Plot with Seaborn (hue creates separate bars for each boolean value)\n",
        "sns.countplot(\n",
        "    data=local_df,\n",
        "    x=\"numEvents\",\n",
        "    hue=\"abnormal\",\n",
        "    palette=[\"blue\", \"red\"],\n",
        "    order=local_df[\"numEvents\"].value_counts().iloc[:15].index,\n",
        ")\n",
        "plt.title(\"Distribution of log sequence length by abnormality\")\n",
        "plt.xlabel(\"Events#/Sequence\")\n",
        "plt.ylabel(\"Count\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gzFpxV9Mh_Ej"
      },
      "source": [
        "## Create and store embeddings in vector index"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U1tK1ZSGh_Ej"
      },
      "source": [
        "### Explain log sequences\n",
        "Prompt Gemini 2.0 to translate log sequences into natural language"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l7A220hCh_Ek"
      },
      "source": [
        "#### Create the remote model for Gemini 2.0 in BigQuery"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8c6ac9ace0f4"
      },
      "outputs": [],
      "source": [
        "#"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6PJ71LkVh_Ek"
      },
      "outputs": [],
      "source": [
        "%%bigquery\n",
        "CREATE OR REPLACE MODEL `vs_logs_demo.gemini_1_5_flash`\n",
        "REMOTE WITH CONNECTION `us.bq-llm-connection`\n",
        "OPTIONS (endpoint = 'gemini-2.0-flash')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tejGKDOtcgOy"
      },
      "source": [
        "#### Prepare prompt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "w7Rqy_tJc_aE"
      },
      "outputs": [],
      "source": [
        "# Prompt for Gemini\n",
        "prompt_prefix = f\"\"\"\\\n",
        "You're an IT expert and administrator of a Hadoop Distributed File System or HDFS cluster.\n",
        "You are provided with a sequence of HDFS logs in chronological order, where each log line contains the log source component, followed by the log severity and the log content itself.\n",
        "Describe succinctly in plain english the events corresponding to this sequence of logs.\n",
        "Do not repeat the raw logs in your explanation. Do not speculate, explain or make stuff up.\n",
        "Be concise in your answer and combine similar events together.\n",
        "Output must be formatted as an ordered numbered list and without any preamble.\n",
        "\"\"\"\n",
        "\n",
        "# One-shot example where the input is the raw log sequence for one session,\n",
        "# and the output is the corresponding log explanation in natural language.\n",
        "example_input = f\"\"\"\\\n",
        "dfs.DataNode$DataXceiver INFO Receiving block blk_4970672161979262840 src: /10.251.111.130:54916 dest: /10.251.111.130:50010\n",
        "dfs.FSNamesystem INFO BLOCK* NameSystem.allocateBlock: /user/root/rand5/_temporary/_task_200811101024_0011_m_000844_0/part-00844. blk_4970672161979262840\n",
        "dfs.DataNode$DataXceiver INFO Receiving block blk_4970672161979262840 src: /10.251.66.102:49145 dest: /10.251.66.102:50010\n",
        "dfs.DataNode$DataXceiver INFO Receiving block blk_4970672161979262840 src: /10.251.111.130:34614 dest: /10.251.111.130:50010\n",
        "dfs.DataNode$PacketResponder INFO PacketResponder 1 for block blk_4970672161979262840 terminating\n",
        "dfs.DataNode$PacketResponder INFO PacketResponder 0 for block blk_4970672161979262840 terminating\n",
        "dfs.DataNode$PacketResponder INFO PacketResponder 2 for block blk_4970672161979262840 terminating\n",
        "dfs.DataNode$PacketResponder INFO Received block blk_4970672161979262840 of size 67108864 from /10.251.111.130\n",
        "dfs.FSNamesystem INFO BLOCK* NameSystem.addStoredBlock: blockMap updated: 10.251.111.130:50010 is added to blk_4970672161979262840 size 67108864\n",
        "dfs.FSNamesystem INFO BLOCK* NameSystem.addStoredBlock: blockMap updated: 10.251.66.102:50010 is added to blk_4970672161979262840 size 67108864\n",
        "dfs.DataNode$PacketResponder INFO Received block blk_4970672161979262840 of size 67108864 from /10.251.66.102\n",
        "dfs.DataNode$PacketResponder INFO Received block blk_4970672161979262840 of size 67108864 from /10.251.111.130\n",
        "dfs.FSNamesystem WARN BLOCK* NameSystem.addStoredBlock: Redundant addStoredBlock request received for blk_4970672161979262840 on 10.251.194.147:50010 size 67108864\n",
        "dfs.FSNamesystem INFO BLOCK* NameSystem.addStoredBlock: blockMap updated: 10.251.194.147:50010 is added to blk_4970672161979262840 size 67108864\n",
        "dfs.FSNamesystem INFO BLOCK* NameSystem.delete: blk_4970672161979262840 is added to invalidSet of 10.251.66.102:50010\n",
        "dfs.FSNamesystem INFO BLOCK* NameSystem.delete: blk_4970672161979262840 is added to invalidSet of 10.251.111.130:50010\n",
        "dfs.FSNamesystem INFO BLOCK* NameSystem.delete: blk_4970672161979262840 is added to invalidSet of 10.251.194.147:50010\n",
        "dfs.FSDataset INFO Deleting block blk_4970672161979262840 file /mnt/hadoop/dfs/data/current/subdir59/blk_4970672161979262840\n",
        "dfs.FSDataset INFO Deleting block blk_4970672161979262840 file /mnt/hadoop/dfs/data/current/subdir9/blk_4970672161979262840\n",
        "dfs.FSDataset INFO Deleting block blk_4970672161979262840 file /mnt/hadoop/dfs/data/current/subdir31/blk_4970672161979262840\n",
        "\"\"\"\n",
        "\n",
        "example_output = f\"\"\"\\\n",
        "1. The NameNode allocates a new block for a file in the user directory.\n",
        "2. Three DataNodes are receiving the block.\n",
        "3. The DataNodes packet responders for that block are terminating.\n",
        "4. The DataNodes receives the block.\n",
        "5. The NameNode updates block map after receiving confirmation that the block has been received by the three DataNodes.\n",
        "6. The NameNode reports a warning that it received a redundant confirmation from one of the DataNodes.\n",
        "7. The NameNode deletes the block and marks it as invalid on all three DataNodes.\n",
        "8. The DataNodes delete the block from their local storage.\n",
        "\"\"\"\n",
        "\n",
        "# Escape newlines before subsequent use in f-strings\n",
        "prompt_prefix = prompt_prefix.replace(\"\\n\", \"\\\\n\")\n",
        "example_input = example_input.replace(\"\\n\", \"\\\\n\")\n",
        "example_output = example_output.replace(\"\\n\", \"\\\\n\")\n",
        "\n",
        "params = {\n",
        "    \"prompt_prefix\": prompt_prefix,\n",
        "    \"example_input\": example_input,\n",
        "    \"example_output\": example_output,\n",
        "}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J53myNZ5cMBn"
      },
      "source": [
        "Let's save the prompt and one-shot example in a new BigQuery table `hdfs_prompts`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "i8t9yIgRr9n-"
      },
      "outputs": [],
      "source": [
        "sql = f\"\"\"\\\n",
        "CREATE OR REPLACE TABLE `vs_logs_demo.hdfs_prompts` AS (\n",
        "  SELECT * FROM UNNEST([STRUCT(\n",
        "    'Explain HDFS log sequences' AS task,\n",
        "    \"{prompt_prefix}\" AS prompt_prefix,\n",
        "    \"{example_input}\" AS example_input,\n",
        "    \"{example_output}\" AS example_output\n",
        "  )])\n",
        ");\n",
        "\"\"\"\n",
        "\n",
        "query_job = client.query(sql)\n",
        "print(query_job.result())  # Wait for the job to complete.\n",
        "print(f\"Created Table {PROJECT_ID}.vs_logs_demo.hdfs_prompts\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2VnrZSeKZfHJ"
      },
      "source": [
        "As an example, let's take the following 'normal' raw log sequence for specific blockID `blk_-3750780870143089898`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wwdVnGOnZxeT"
      },
      "outputs": [],
      "source": [
        "dfs.DataNode$DataXceiver INFO Receiving block blk_-3750780870143089898 src: /10.251.67.211:46933 dest: /10.251.67.211:50010\n",
        "dfs.FSNamesystem INFO BLOCK* NameSystem.allocateBlock: /user/root/randtxt5/_temporary/_task_200811101024_0012_m_000993_0/part-00993. blk_-3750780870143089898\n",
        "dfs.DataNode$DataXceiver INFO Receiving block blk_-3750780870143089898 src: /10.251.42.207:51200 dest: /10.251.42.207:50010\n",
        "dfs.DataNode$DataXceiver INFO Receiving block blk_-3750780870143089898 src: /10.251.67.211:50346 dest: /10.251.67.211:50010\n",
        "dfs.DataNode$PacketResponder INFO PacketResponder 0 for block blk_-3750780870143089898 terminating\n",
        "dfs.DataNode$PacketResponder INFO PacketResponder 2 for block blk_-3750780870143089898 terminating\n",
        "dfs.FSNamesystem INFO BLOCK* NameSystem.addStoredBlock: blockMap updated: 10.251.42.207:50010 is added to blk_-3750780870143089898 size 67108864\n",
        "dfs.FSNamesystem INFO BLOCK* NameSystem.addStoredBlock: blockMap updated: 10.251.67.211:50010 is added to blk_-3750780870143089898 size 67108864\n",
        "dfs.DataNode$PacketResponder INFO Received block blk_-3750780870143089898 of size 67108864 from /10.251.67.211\n",
        "dfs.FSNamesystem INFO BLOCK* NameSystem.addStoredBlock: blockMap updated: 10.251.194.129:50010 is added to blk_-3750780870143089898 size 67108864\n",
        "dfs.DataNode$PacketResponder INFO PacketResponder 1 for block blk_-3750780870143089898 terminating\n",
        "dfs.DataNode$PacketResponder INFO Received block blk_-3750780870143089898 of size 67108864 from /10.251.67.211\n",
        "dfs.DataNode$PacketResponder INFO Received block blk_-3750780870143089898 of size 67108864 from /10.251.42.207\n",
        "dfs.FSNamesystem INFO BLOCK* NameSystem.delete: blk_-3750780870143089898 is added to invalidSet of 10.251.42.207:50010\n",
        "dfs.FSNamesystem INFO BLOCK* NameSystem.delete: blk_-3750780870143089898 is added to invalidSet of 10.251.67.211:50010\n",
        "dfs.FSNamesystem INFO BLOCK* NameSystem.delete: blk_-3750780870143089898 is added to invalidSet of 10.251.194.129:50010\n",
        "dfs.FSNamesystem INFO BLOCK* ask 10.251.194.129:50010 to delete  blk_-3750780870143089898 blk_8839488796462659348 blk_-1076638448786103147 blk_-2607303556568476338 blk_-872077376619694315 blk_3658484321727696675 blk_-8783295298979970351 blk_7253228695508151434 blk_8353593257930202451 blk_6896918372378359715 blk_8476431461885450664 blk_-453468701147780383 blk_2747276825479054342 blk_7831811674826419739 blk_-4676554322964835078 blk_838168686993939059 blk_-3667727247990911503 blk_554950885642981306 blk_3690236354878191248 blk_3594952792786303214 blk_710500522210229744 blk_-641656950218902981 blk_-8914315030958124984 blk_8473337661129085162 blk_8747565120239750968 blk_8483881408521191191 blk_970871787748196992 blk_-1238776083272375823 blk_-4355328787228390393 blk_-6232254141637974415 blk_2851445563013723058 blk_4729371515425302371 blk_-8138185632733985405 blk_3530286662170963267 blk_6705596331043244121 blk_5856355648225187587 blk_-5271003563714979606 blk_6099159980310485523 blk_5770736739131705773 blk_-4630334698677080743 blk_-316813592070079363 blk_-8376103172399604209 blk_-5004999163141797571 blk_-1184480050082523451 blk_-8644451221914479826 blk_-4421425625682059690 blk_8309579147878024429 blk_3172294788463323663 blk_-426829215056729544 blk_-785413567218674273 blk_2115841663373157801 blk_2855785622370264073 blk_8386172561326404060 blk_6747728035734009764 blk_7206820189825343828 blk_9222274141331127342 blk_-7791132919862479353 blk_-1813922770106404412 blk_-6794746648243431237 blk_-3114372317175128819 blk_-5375459594742433201 blk_6912573532770191892 blk_124792821150689426 blk_8787609558519171611 blk_-5450607639120667562 blk_769133640763536215 blk_4689006757714287009 blk_-2322954586860795404 blk_-3083400559019231217 blk_-2398840409229247956 blk_4606567481735284177 blk_-1098865217826643562 blk_-3075042532557633130 blk_6737487517130970246 blk_3299232526334609785 blk_-1866148958272946201 blk_4803758272177687030 blk_2068490022276187676 blk_-4724682685867818017 blk_-1311156078103024510 blk_-1781420834572645132 blk_-8680808247265383818 blk_7865294689332645011 blk_-7864388093305159311 blk_-7259235578951211339 blk_4437793449639312068 blk_2972756848238658967 blk_160665971665268866 blk_7862577900146719312 blk_2141823066215925124 blk_4044494381360076828 blk_868975602193282101 blk_-8493544210473294093 blk_-3909434520726038447 blk_-2397800658299122228 blk_-6929912433144478107 blk_-2022835339724037507 blk_-251514397381670680 blk_-4491187364929180193 blk_-4766279731310845414\n",
        "dfs.FSDataset INFO Deleting block blk_-3750780870143089898 file /mnt/hadoop/dfs/data/current/subdir32/blk_-3750780870143089898\n",
        "dfs.FSDataset INFO Deleting block blk_-3750780870143089898 file /mnt/hadoop/dfs/data/current/subdir16/blk_-3750780870143089898\n",
        "dfs.FSDataset INFO Deleting block blk_-3750780870143089898 file /mnt/hadoop/dfs/data/current/subdir55/blk_-3750780870143089898"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fbR3E9SCaOsD"
      },
      "source": [
        "The Gemini prompt above converts those raw log entries into a sequence of events in natural language which captures concisely the execution path including detailed semantics information while excluding specific identifiers:\n",
        "\n",
        "```\n",
        "1. The NameNode allocates a new block for a file in the user directory.\n",
        "2. Three DataNodes are receiving the block.\n",
        "3. The NameNode updates block map after receiving confirmation that the block has been received by the three DataNodes.\n",
        "4. The NameNode asks one of the DataNodes to delete the block.\n",
        "5. The DataNodes delete the block from their local storage.\n",
        "```"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GA6jBUfF59yX"
      },
      "source": [
        "#### Run on subset of logs"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OPRRTIHjNm86"
      },
      "source": [
        "The following **may take 6 hours or less** depending on your Vertex AI quota of requests per minute for `gemini-2.0-flash` base model. The following query goes over a small subset of the dataset: 70k log sequences will be translated into natural language summary. At the time of this writing, the default rate limit for `gemini-2.0-flash` in `us-central1` region is **200 requests/min**. To speed up this step, you can [request quota increase](https://cloud.google.com/vertex-ai/generative-ai/docs/quotas#view-quotas-in-console) for Vertex AI endpoint for `gemini-2.0-flash` for your specific region and project. If you do increase that Vertex AI quota, send an email to bqml-feedback@google.com to adjust and increase your BigQuery ML quota for `ML.GENERATE_TEXT` calls to that base model, since BigQuery ML rate-limits the calls to Vertex AI endpoint accordingly.\n",
        "\n",
        "The following SQL code firsts create the table `hdfs_full_explained`, then creates a procedure to generate content from Gemini and inserts into the destination table. You call the procedure iteratively to process data in batches and avoid losing processed results due to runtime error like query timeout or quota exhaustion, both of which are unlikely with smaller batches."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LoCQyX7sMhvX"
      },
      "outputs": [],
      "source": [
        "%%bigquery\n",
        "CREATE OR REPLACE TABLE `vs_logs_demo.hdfs_full_explained`\n",
        "(\n",
        "  sessionId INT64,\n",
        "  blockId STRING,\n",
        "  abnormal BOOL,\n",
        "  numEvents INT64,\n",
        "  eventSequence STRING,\n",
        "  eventTemplateSequence STRING,\n",
        "  status STRING,\n",
        "  response STRING,\n",
        "  input_tokens INT64,\n",
        "  output_tokens INT64,\n",
        "  total_token_count INT64\n",
        ")\n",
        "PARTITION BY RANGE_BUCKET(sessionId, GENERATE_ARRAY(0, 600000, 10000))\n",
        "CLUSTER BY abnormal, numEvents;"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ye-yBze9f8Kh"
      },
      "outputs": [],
      "source": [
        "params[\"prompt_prefix\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "o8jZ9EnLMs46"
      },
      "outputs": [],
      "source": [
        "%%bigquery\n",
        "CREATE OR REPLACE PROCEDURE `vs_logs_demo.explain_hdfs_logs_test`(\n",
        "  start_row INT64,\n",
        "  batch_size INT64\n",
        ")\n",
        "BEGIN\n",
        "  INSERT INTO `vs_logs_demo.hdfs_full_explained`\n",
        "  SELECT\n",
        "    sessionId, blockId, abnormal, numEvents, eventSequence, eventTemplateSequence,\n",
        "    ml_generate_text_status AS status,\n",
        "    LAX_STRING(ml_generate_text_result.candidates[0].content.parts[0].text) AS response,\n",
        "    LAX_INT64(ml_generate_text_result.usage_metadata.prompt_token_count) AS input_tokens,\n",
        "    LAX_INT64(ml_generate_text_result.usage_metadata.candidates_token_count) AS output_tokens,\n",
        "    LAX_INT64(ml_generate_text_result.usage_metadata.total_token_count) AS total_token_count\n",
        "  FROM\n",
        "    ML.GENERATE_TEXT(\n",
        "      MODEL `vs_logs_demo.gemini_1_5_flash`,\n",
        "      (\n",
        "        SELECT\n",
        "          -- A prompt for HDFS logs explanation with one-shot example.\n",
        "          sessionId, blockId, abnormal, numEvents, eventSequence, eventTemplateSequence,\n",
        "          CONCAT(llm.prompt_prefix,\n",
        "            \"Here is one-shot example:\\n\",\n",
        "            \"Log sequence:\\n\", llm.example_input,\n",
        "            \"Log sequence summary:\\n\", llm.example_output,\n",
        "            \"Here is the input:\\n\",\n",
        "            \"Log sequence: \", eventSequence, \"\\n\",\n",
        "            \"Answer: \"\n",
        "        ) AS prompt\n",
        "        FROM `vs_logs_demo.hdfs_full_labelled`, `vs_logs_demo.hdfs_prompts` AS llm\n",
        "        WHERE\n",
        "          llm.task = \"Explain HDFS log sequences\"\n",
        "          AND sessionId BETWEEN start_row AND start_row + batch_size -1\n",
        "      ),\n",
        "      STRUCT(\n",
        "        0.2 AS temperature,\n",
        "        2048 AS max_output_tokens, -- limit response to ~1400 words\n",
        "        FALSE AS flatten_json_output\n",
        "      )\n",
        "    );\n",
        "END;"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Msl7KJBi90Nc"
      },
      "outputs": [],
      "source": [
        "%%bigquery\n",
        "-- Process 70k sequences with batches of 20k at a time to stay well within #rows/job quota\n",
        "-- for ML.GENERATE_TEXT (72k with Gemini 2.0, 21.6k with Gemini 2.0)\n",
        "-- https://cloud.google.com/bigquery/quotas#cloud_ai_service_functions\n",
        "BEGIN\n",
        "  CALL `vs_logs_demo.explain_hdfs_logs`(1, 20000);\n",
        "  CALL `vs_logs_demo.explain_hdfs_logs`(20001, 20000);\n",
        "  CALL `vs_logs_demo.explain_hdfs_logs`(40001, 20000);\n",
        "  CALL `vs_logs_demo.explain_hdfs_logs`(60001, 10000);\n",
        "END;"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0H5-RmNwZGBp"
      },
      "source": [
        "To process even larger datasets, you can use the procedure iteratively  sequentially while accounting for the quota limit of 72k rows per job.\n",
        "Note the cumulative time limit for a multi-statement query in BigQuery is 24 hours. If the above code times out (unlikely with small 20k batches for a total of 70k), simply comment out the successfully completed batches before running it again so it can resume after that batch."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sdGQq2Rwh_Ek"
      },
      "source": [
        "### Generate embeddings for log sequences"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EjbNVI1Fh_El"
      },
      "source": [
        "#### Create the remote model for generating text embeddings"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "01yw0xUoh_El"
      },
      "outputs": [],
      "source": [
        "%%bigquery\n",
        "CREATE OR REPLACE MODEL `vs_logs_demo.text_embedding`\n",
        "REMOTE WITH CONNECTION `us.bq-llm-connection`\n",
        "OPTIONS (endpoint = 'text-embedding-005')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gZnn6PJXU5sY"
      },
      "source": [
        "#### Generate embeddings"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EbUr8Zvbddwn"
      },
      "source": [
        "Let's generate vector embedding for each log sequence to capture the semantic meaning of the Gemini-produced sequence of operations over each `blockId`.\n",
        "\n",
        "The following **will take several minutes** depending on your quota of requests per minute for `text-embedding` base model. At the time of this writing, the default rate limit for `text-embedding` in `us-central1` region is 1500 requests/min.\n",
        "\n",
        "The following query processes the entire `hdfs_full_explained` table. Unlike the previous section, where `ML.GENERATE_TEXT` was run in iterative batches, we run `ML.GENERATE_TEXT_EMBEDDING` as one job given the higher limit of 2,700,000 rows per job, that is well above our dataset of 70,000 session records.\n",
        "\n",
        "\n",
        "**Important Note:** we use `CLUSTERING` embedding task type which is best suited for (clustering-based) anomaly detection task, as opposed to other tasks like document retrieval (RAG) or semantic similarity search."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_qD7SJeeh_El"
      },
      "outputs": [],
      "source": [
        "%%bigquery\n",
        "CREATE OR REPLACE TABLE `vs_logs_demo.hdfs_full_embeddings` AS (\n",
        "  SELECT\n",
        "    * EXCEPT(ml_generate_embedding_result, ml_generate_embedding_status, ml_generate_embedding_statistics),\n",
        "    ml_generate_embedding_status AS status,\n",
        "    LAX_INT64(ml_generate_embedding_statistics.token_count) AS token_count,\n",
        "    LAX_BOOL(ml_generate_embedding_statistics.truncated) AS truncated,\n",
        "    ml_generate_embedding_result AS embeddings\n",
        "  FROM\n",
        "    ML.GENERATE_EMBEDDING(\n",
        "      MODEL `vs_logs_demo.text_embedding`,\n",
        "      (\n",
        "        SELECT\n",
        "          # sessionId, timeInterval, numEvents, sequence, abnormal,\n",
        "          sessionId, blockId, abnormal, numEvents, eventSequence, eventTemplateSequence,\n",
        "          response as content\n",
        "        FROM `vs_logs_demo.hdfs_full_explained`\n",
        "        -- skip records where previous ml.generate_text step failed due to quota exhaustion\n",
        "        WHERE status=\"\"\n",
        "        ORDER BY sessionId ASC\n",
        "        LIMIT 70000\n",
        "      ),\n",
        "      STRUCT(\n",
        "          TRUE AS flatten_json_output,\n",
        "          'CLUSTERING' as task_type\n",
        "      )\n",
        "    )\n",
        ")\n",
        "PARTITION BY RANGE_BUCKET(sessionId, GENERATE_ARRAY(0, 600000, 10000))\n",
        "CLUSTER BY abnormal, numEvents;"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q3hRmLiY2O0w"
      },
      "source": [
        "### Create vector index"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bH9FNwlfbUmv"
      },
      "outputs": [],
      "source": [
        "%%bigquery\n",
        "CREATE VECTOR INDEX index_treeah_1000 ON `vs_logs_demo.hdfs_full_embeddings`(embeddings)\n",
        "# STORING (abnormal, sessionId, numEvents)\n",
        "OPTIONS (index_type = 'TREE_AH',\n",
        "  distance_type = 'COSINE',\n",
        "  tree_ah_options = '{\"leaf_node_embedding_count\": 1000}');"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EGO4QPhZh_Em"
      },
      "source": [
        "Inspect vector index progress, and total storage (chargeable) from INFORMATION_SCHEMA.VECTOR_INDEXES view:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IOmgcRWfh_Em"
      },
      "outputs": [],
      "source": [
        "%%bigquery\n",
        "SELECT\n",
        "  table_name,\n",
        "  index_name,\n",
        "  coverage_percentage,\n",
        "  unindexed_row_count,\n",
        "  last_refresh_time,\n",
        "  total_logical_bytes,\n",
        "  total_storage_bytes\n",
        "FROM `vs_logs_demo.INFORMATION_SCHEMA.VECTOR_INDEXES`\n",
        "WHERE index_status = 'ACTIVE';"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ul3FaK8Ph_Em"
      },
      "source": [
        "## Visualize clusters of log sequences"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c6qQx9zHlXoJ"
      },
      "source": [
        "As log sequences are projected into the 768-dimension vector space, similar log sequences are clustered as \"neighbors\" in the sense they share a similar sequence of what actually occurred.\n",
        "\n",
        "Let's do a first-order evaluation to see how the clustering is performing qualitatively. Specifically, we're looking at how well-defined the clusters are and how much separation there is between clusters."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XYdXKXd1fzoy"
      },
      "source": [
        "We use dimensionality reduction for visualizing in 2D space the clusters of HDFS log sequences embeddings by going from 768 to 2 dimension vectors."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PIjQeAU78HeB"
      },
      "source": [
        "### Visualize embeddings"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nM03efDp8GPk"
      },
      "outputs": [],
      "source": [
        "client = bigquery.Client()\n",
        "\n",
        "# sql = f\"\"\"\n",
        "# SELECT *\n",
        "# FROM `vs_logs_demo.hdfs_full_embeddings`\n",
        "# TABLESAMPLE SYSTEM (10 PERCENT)\n",
        "# \"\"\"\n",
        "# df = client.query(sql).to_dataframe()\n",
        "\n",
        "table_id = \"vs_logs_demo.hdfs_full_embeddings\"\n",
        "df = client.list_rows(table_id).to_dataframe()\n",
        "df.head(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JsVt6qXph_Eo"
      },
      "outputs": [],
      "source": [
        "print(df.iloc[70].content)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4PCddtSs8GPl"
      },
      "outputs": [],
      "source": [
        "# Reduce dimensionality using t-SNE. Will take 5 minutes with 10% of the data\n",
        "tsne = TSNE(n_components=2, random_state=0)\n",
        "embeddings_array = np.array(df[\"embeddings\"].to_list(), dtype=np.float32)\n",
        "embeddings_2d = tsne.fit_transform(embeddings_array)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wEx5o4a07Dxd"
      },
      "outputs": [],
      "source": [
        "# Mapping boolean values to colors\n",
        "color_map = {True: \"red\", False: \"blue\"}\n",
        "colors = df[\"abnormal\"].map(color_map)\n",
        "\n",
        "# Create a scatter plot of the embeddings\n",
        "plt.figure(figsize=(10, 8))\n",
        "plt.scatter(embeddings_2d[:, 0], embeddings_2d[:, 1], c=colors, s=25, alpha=0.7)\n",
        "\n",
        "plt.title(\"Log Embeddings Clusters\")\n",
        "plt.xlabel(\"TSNE-1\")\n",
        "plt.ylabel(\"TSNE-2\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ecf8102c85e7"
      },
      "source": [
        "Results should be rendered in a scatter plot as follows:"
      ]
    },
    {
      "attachments": {
        "830d2fb9-2eb8-481f-b3ff-47129a741321.png": {
          "image/png": "iVBORw0KGgoAAAANSUhEUgAAAw0AAAJ3CAIAAABZTSqkAAAAAXNSR0IArs4c6QAAAERlWElmTU0AKgAAAAgAAYdpAAQAAAABAAAAGgAAAAAAA6ABAAMAAAABAAEAAKACAAQAAAABAAADDaADAAQAAAABAAACdwAAAAC95pypAABAAElEQVR4AezdB7gdVdU38EkPBEKvUoJ0kI50BRVEQQXFgqKCDRBQ7IivBbvYeRFU7CIvosIHgkqT3ouI9B6pUqSXJCS532/uSiYn55x7bjs3t2St5zz3zpnZs8t/77PXf9Zae8+ojo6OIiURSAQSgUQgEUgEEoFEoAGB0Q1n8kQikAgkAolAIpAIJAKJQIlA8qQcB4lAIpAIJAKJQCKQCDRHIHlSc1zybCKQCCQCiUAikAgkAsmTcgwkAolAIpAIJAKJQCLQHIHkSc1xybOJQCKQCCQCiUAikAgkT8oxkAgkAolAIpAIJAKJQHMEkic1xyXPJgLDDoHZs2dPmzZt+vTpA7TZx4wZM55tkJkzZ/YWqE984hN77LHHww8/3Nsba9M/99xz3/ve997+9rf/+9//rj0fx1ddddVOO+30pz/9ydf//Oc/a6yxxi9/+cvGZAN0ZtasWS+88IIakueff16P6Jooa/To0T/60Y/6XK6cAT5A/dvnWuWNicDIRmDsyG5eti4RWHgQuP/++7/0pS8tt9xyn/3sZ5deeum2N/zoo4/+1Kc+teKKK1L2VeY//vGPd9ttt7Fjh+5Mom4bbLDBkksuWdV5QA+eeuqp888/Hy27++67X3zxxaWWWmqrrbbab7/9Nt54436ihHvJ+cEHH3zTm960/PLLD2grMvNEIBGoEBi6s1tVxTxIBBKBIYLAqFGjfvrTny622GJVfTbccMMxY8ZUX4fgwTLLLHPqqacumEoytx111FEsRnvttdfee++NnKE1F1988R/+8IdVV10Vhe0PPljXtddee8MNNzCV9ZYnMWjpO9KfCuS9icDCiUDypIWz37PVCxcCXGb33HPPHXfcwQeE5Wy00UYrr7xyQOASy8ftt9/Op7PKKqssscQS//jHP974xjdOmjSpKUaveMUr2EjqLnGiXXDBBVtuueVjjz2GGUycOPFlL3uZIm655Za77rpLzjxfm2yySa0h6t5776X1uaXwmE033bTKk+vw5ptvdpWDCc9gCqqq6tI///nPBx54gGGGWatyZkVlNPCmm27SQHSE6aWqobvOOeec9ddff+211+YIu+KKK2QLBE1WhMxf/vKXR8XUU+ZKVysGObecd95522233eqrr+4Sc91tt932zDPPSAwlVqJavhjFKYjV7dBDD/30pz89YcKEOPmWt7yFc7AOz0cfffT6669XJZlHsuuuuw50u+++u69KUZbKqOH48eNXW201fFT3uUVWp59+uubrROBIHHVG0aRcZ511QO3Aef340EMPsWPdeuutruod7OrGG2/UEJRL9VA3bazqGdXIv4lAIlCHQPKkOkDyayIw0hCgazES+lukDtWOymy++eaHH374S17yEuqffeK73/0uXUuJjhs3Dof4yU9+gqbU6fUKFBQEDaq+UslMNcjQO9/5zo985CNPPvkkHU/fv+pVr6KYkaf//ve/eBja8c1vfhNVihvxqmOOOQaDob8xhve85z377rvv4osvjuWcdtppJ5xwAsuHyrh36623PvDAA6dMmaKqZ5xxBlMNkoRa+atdVTXuvPPOb3/721dfffWaa66pSkTmcZUjDHH5whe+gJRo+3e+8x1EBz/QZPVEtn7wgx+gShJrNWuQTEChUajel7/85T/+8Y+oDHbyv//7v2jf5MmTpcS3apsTBamhxAgfHGrJh7aTSFP9hZgcDj744Ioniab685//jCcFsfvVr34FhEUXXRQm6qN01UMEIYyNwUovaIWGq7Pc9OzTTz8t5SGHHKI58DnppJN++9vfAhaDXGSRRXQ3lslPClhf8eO11loLMs5XtcqDRCARaEQgeVIjJnkmERhRCCAHP//5zzGSww47jPmEHUWYEbX6rW996/HHH//d736HSXzmM5+hsBGaL37xiy0aL4L4c5/7XEUCaOv999+fDcMtLtHiri677LJ/+9vfcB2E44ADDmAIwYoOOuigU045peJJFDYi9da3vpU6l1g0D539ute9Tvz1cccdx5q144474gE4HH7AyvXhD3+YUeTYY4/FdT74wQ9iD5dffjkSs/322ysa19EKth+ET2Xuu+8+KR955JGmDcGuXHrzm9+MQKA7OA2e4XY2pLPOOou1Sf7I2RNPPIF4RQ4IkKZdeeWVmuOSr1rEolOXP1aHr2y22WasTXWXevVV5ggTaog+ykrH4UbIDfYDIjjoPp2FLCJ8aB+jEdbLiqY38chf/OIXQHBVoW7Urk9+8pPonTPf//73dRlbl7/azqrkoFd1y8SJwEKIQPKkhbDTs8kLFwJ4Eh3PdPGa17wGL1lvvfUQAtpU0DfNetlll73tbW977WtfywbDPnHmmWf+61//agEQgw2aEgkcsEzEMePHzjvvjP04oNqFBNHWr371q6lnHIiTiIJHaMLDRam/613vYrBxL8bGkYSg7LrrrhdddBFCsMsuu+BGLsmf2+iaa65BQVAoFqAjjzySmUoRLDSqGkWzk7lRODljjNsVp104Vlyt+6sOcrDgjunFJbE+7C4OECNR0i5hb4xqziA9f/nLXxwoDrtCONyCqMEwau5SrQQpWWmllWpP9uE41hVqIKoUlYxMcB0nMRtuOL3gJF7LCoX9IGe+sgwhjgLI4B88CZnTHC5CV2WLG+G48IkGRrb5NxFIBFojkDypNT55NREY3gigBRxbTCAsEBS8xmASW2yxxc9+9jN2CE40xiSkBElySQKhQi0ajDG8733vq2KJfK1sS+4SLuOMA9SH4EnBopyk79EOHqXgWNQ8lR+JqX91UxnkhinowgsvFAStkvJReefpfpXkI5MSC6mKYKkSxCOZNIiURgUJ0xZMIoiCq3UiZ+1l9IrzDGzol2NQOOCRrKxBMow0stUWrOVjH/sYk9grX/lK5qhgcrWZK1dKbrLak304ljO6yRvIZobioLCvf/3ro2l1uQmlggy7msRxCQ5YI8YGAWeQKjWPSxouUuqrX/0qGqppWCneDNK6PPNrIpAI1CGQPKkOkPyaCIwoBLjDkCRaNkhStC1YURXfU7sWLAhKCwjELXUVuhTZuheVUaKvwWniq5rgPZGzysSlSKwCKskNpErbbLPNBz7wgVr9zfiEHkkgWe1dVVXdJfOqOHkqvbZRUWj8lYPSK9ohmXtdiuo5XxVRNcdVljYOQQYnpi/+OA4+B2xdtTmvsMIKGCSzWe3Jro6rUqoEGhjHyOWHPvShbbfdVjwWpyT7EH7DM1ilrA5wMm356Ec/yspVnXSAd0b+rtYa/DjyZMvUx6AolOoNb3jDN77xjYxPqoUujxOBRgSSJzVikmcSgZGDAB7AdoJJiHpBBahPf5lhaFAKcurUqdgAI0R1SVxwnxvfqPu7yordqLK7OGAr4vDCh4iIab6wWmtN1I19iJmkis52oPKRv7uYr8RdSekMNsbKwtvYVelNz8OBiYtNS33CSGZ9WZVS09AgS/0JsxN/ol0uf/Ob31QJHGAkzD/i02HIYFahoT6kluRJrDg8T1kV8qKtq9xcYtkiwsV++MMfih4TR8V+Ft1XJVMKYqcfRXRVxUWGVZrqvDPqwOtqey0hSn//+9/lKdB+n332qRLnQSKQCDQiMG+/uMZreSYRSASGHQL8R5Q96hMi7EYwCnsDfxbF75KQZGvK+LaYhVwSOWSDHyddwp9E5NCstcq1DgGLqrCQSkLT16Xp9quanH322agbNsO7ZKU6fxYtjmc4aT1XOI9cxR6s85Ih0qDCxx9/vEuaJoiqik9iPuEu/P3vf4+gaC/CxFiiYt1WozYBKNhaRGsJdZI/6maxWCRg6VErtEx9cDUnEangUrU5OGYJQ0Te+973QlImEosZFxx24oknqlhtYsVhfpbuyxbyFiSee+65kQDCWq2ZbnEJzUWbgOMvOugkRyr8NZBBS52tjOPTlJJwQSq6advFV8lWAjkoQm4i5f2trVUeJwKJQCMCaU9qxCTPJALDGAHLvijCSouLtrEGzUItEdDWPdkyxx48gnIEtWgkG4llX0cccQQDg/BejERciwhoWrkpBGwVFqDV7gsgeoanrGniFieZRtAaq94YWi655BIOIPlIL2gGhbJoC4tiUrLFAMuTS5avW+3FlmNlnASoAzJURd6orQ2vObze/e53C2ZCUNylaS0q0HgJUKohnFwEjzBtmQSGMhcBjbqhI+imiB9EhDUIbo2ZrLvuurxynFmuiq3mPcRpsBPr1CpPX9yldU6yFSFkmqNRLGqOXdUL4o1QGZ3FEHjppZe+4x3vQGdjIwCUCz42SQKLxipO/+JnOJMiME6+PzsOsLHVVQ+10ulCvHUxHoyZgVShdcnyayKQCNQhMMYUWXcqvyYCicBwRCBCfHh/xEpT5CH0ujX2OBA7B1KiXQwzIloE3DimWXEmhMMlpgWam2rHtLh7KqZVQcEgQQdLiTdUwvVD70bwULVPNO0uGTLE0oNnYFdWWnEP0cr8fdgPrU/3owIu4UZYRSyzVwfVEzEd+bO42ObRMjoeMVVFBSTjcWNYsmBNu4SHM0Rpssy1SObKlYPMkQ+XtE7d2NhYqtQzKi/evOJYKsa0I1BaM92CIUWFsRzB1MxXtgmAnhKJrCRzr30KVKxCpvbgpS99KRywLuFW0qvAnnvuKcAogt9ZmGzUieUoRZ3FXbH96CM8D1y4nauQVyXtBYKuxIdsiSRD5AYOMtQc1YaSsmSC7qBQErtRJnxwkT9KJH/1DKORv+qgXBUzNnbYYQew677ayudxIpAINCJQBis0ns0ziUAisJAggDqYBKhP7UVB3v/+93NpMSm1cL2NVGRqoYCJCCT7EnFpIVgjtcnZrkQgEegWgfS7dQtRJkgERjICPD4MSCwTDBh2hhTey5i0EJIkfcxbJ66Z0YXpSKiTzTnZpRhsRnL3Z9sSgUSgOwSSJ3WHUF5PBEY0Apw4uBEHE8eNhooEWmgXQHGTecmabS35wrgO+fXYk8LSNqKHQDYuEUgEWiGQfrdW6OS1RGDEI4ATWMbF48brJGxFBIxwmRHf6qYNFLhjyZjQH4QJPRIS1NVmlU1vz5OJQCIwIhFInjQiuzUblQgkAolAIpAIJAJtQGAE+t0yMr0N4yKzSAQSgUQgEUgERi4CqIKgzJ60b6TxJFEF8f7LhTMQtSddnmkSgUQgEUgEEoGFHAGhmXYqiV0zWkMx0vxutiex24oAi3jdZuvG59VEIBFIBBKBRCARWNgQYFLxigJLXBt3ZG2EYqTZkwSi2mvuxz/+sQ3uGlubZxKBRCARSAQSgURgIUfAbrf2RaveEt0ajZHGk8LdZnfaHra/NTp5NRFIBBKBRCARSARGGALsST1vUY+CmHqeXaZMBBKBRCARSAQSgURgxCCQPGnEdGU2JBFIBBKBRCARSATajEDypDYDmtklAolAIpAIJAKJwIhBIHnSiOnKbEgikAgkAolAIpAItBmB5EltBjSzSwQSgUQgEUgEEoERg0DypBHTldmQRCARSAQSgUQgEWgzAsmT2gxoZpcIJAKJQCKQCCQCIwaB5EkjpiuzIYlAIpAIJAKJQCLQZgSSJ7UZ0MwuEUgEEoFEIBFIBEYMAoOzH/ezzz579dVXX3PNNY8++uhrX/vaHXfc0bvovJTuN7/5zW233RbgrrTSSvvuu+8KK6zgq2Qnn3zyvffeu+yyy+66667rrbfemDFjRkwfZEMSgUQgEUgEEoFEYGgiMDj2pOeff/7OO+98+umnzznnnH/+85+xg/j06dPPP//8J554YodO2XzzzeNdth0dHd/+9rfPPfdcr2ybOnXq0Ucffffddw9NNLNWiUAikAgkAolAIjCSEBgce9LSSy/9tre9DQG65557atFkVdpoo4322GMPJ72pbfToksZhRYxJTE1bbrkla9PXv/71f/3rX2ussYb33dbem8eJQCKQCCQCiUAikAi0F4HBsSehOEsuueRSSy01YcKE2vY899xz3//+97fYYov3vve9V1111axZs1y9+OKLpdxuu+282haLWnvttW+44Qaeu+rG2bNn89l5/S9ho+rV++2qTPIgEUgEEoFEIBFIBBKBOgQGhyfVVSK+4kzo0QknnPCTn/xEWNJHPvKRW265hc3pgQceYH+KgCR/F1tssaeeeurFF1+sMnnwwQcPPPDA5TqFb+7aa6+tLuVBIpAIJAKJQCKQCCQCfUZgCLmumIve/OY3R0vWX3/9Aw444MILL2Q9YnxiMapaiDnxx/HKVWdWXnnln/70p8cee6wzLFK77bZbdSkPEoFEIBFIBBKBRCAR6DMCQ8ieVNsGRiO+NjHdWNFaa631yCOPCP2WgH/t8ccfX3755WsddmjTxIkTJ3XK4osvnnFLtUjmcSKQCCQCiUAikAj0GYHB4UnsQ3gPxjNt2jQH+BA7EA504403OunrFVdcYWnbOuusg/Rss8020v/lL3+xO8Dll18u9PtlL3tZLIXrc7PzxkQgEUgEEoFEIBFIBLpFYHD8briRHQEEaIslwocQoK233vrVr371UUcdJcqIT01M0mabbWZ/ACvguNUOOuigE088UWQ3w9LGG2+86aab5v5J3XZtJkgERjwCHPJPPlk88USx6KLF8ssXuavaiO/xbGAisOARGByexEq0yiqr2CGJRJunTJliBdxee+1lzRqe9KpXvWrDDTeMTSZ9fc973rPuuusyNfHH4Um2oFzwSGWJiUAiMKQQ4Iq/4oriwguL//ynWGyxYpNNij33LCZPHlJ1zMokAonAsEdgcHiScKKXd0odfq973evqzsTXyZMn77zzzk0v5clEIBFYCBGw4PWaa4pjjikeeqiwu4ivONOjjxaf+ISt1xZCPLLJiUAiMFAIDE580kC1JvNNBBKBhQOBZ54pzj67uP/+YsklS2PSEkuUTrdTTinuvHPhaH+2MhFIBBYUAsmTFhTSWU4ikAi0D4EXXihuvbWwJ3/EJNm6f/HFi8cfL+64o31lZE6JQCKQCBTF4PjdEvlEIBFIBAIBu+7PmFEe4jrjxpV/eyLoETNSzcZqhXzQJgHdKYlAIpAItBGBns1JbSwws0oEEoFEYC4CDz9cnHpqseaaxbLLFvvsU1xyiT3S5l5r+Z/1aIcdyhTPPVfMnFnGJz3ySLH++sVWW7W8LS8mAolAItBLBJIn9RKwTJ4IJAJtQmD69OKDHyze+tYyFtvitZNPLl772vKv890Ku9EuuxS77lqMH188/XQxbVq53k0Qd9qTuoUuEyQCiUCvEEi/W6/gysSJQCLQNgROO60444z5csOQjjiieMUritVXn+984xeL2lZbrTjooGL77ctobnHcm25arLFGY8I8kwgkAolAvxBIntQv+PLmRCAR6DMCX/96k1vvumtOuFKTa/OfQpXsLcmqlJIIJAKJwMAhkH63gcM2c04EEoFWCPTEv9bq/ryWCCQCicDAI5A8aeAxzhISgUSgGQIHHNDkrABty9ZSEoFEIBEYIggkTxoiHZHVSAQWOgQEca+4Yn2rjzqqyPcS1YOS3xOBRGDwEEieNHjYZ8mJwMKNANPRv/5VrlmLN434+stfFnvvXUycuHDjkq1PBBKBoYRAGriHUm9kXRKBhQyB5ZYrzjxzGLS52gyzqusii1SHeZAIJAIjGYG0J43k3s22JQKJQP8RsDkTQ5edmaqPbQjuvrv/GWcOiUAiMAwQSJ40DDopq5gIJAKDhYBXo1x9dbH//vOVb/vvtdee70x+SQQSgZGKQPKkkdqz2a5EIBFoAwLei/KHPzTJp6OjePLJJufzVCKQCIwwBJInjbAOzeYkAolAOxF49tniRz9qkiGedN55Tc7nqUQgERhhCCRPGmEdms1JBBKBBYRAviZlAQGdxSQCg4pA8qRBhT8LTwQSgaGNwNJLFz/5SZMq2stgs82anM9TiUAiMMIQSJ40wjo0m5MIJALtRGDChGKHHYpVV63P85Zb6s/k90QgERiRCCRPGpHdmo1KBBKBtiGw3nrFTTcV3//+nAzHjSvuvLNYd9225Z8ZJQKJwFBGIHnSUO6drNvwQ8Ayctvt2Gb6iCOKyy4rj21RmDKsERgzprBX+Mc/Xojd9pkxo1hzzWHdoKx8IpAI9AKB5Em9ACuTJgKtEaBEr7mm2HTTYpNNii9/udh++zKE5e9/L2bObH1fXk0EEoFEIBEYoggkTxqiHZPVGo4ITJ1aHHZYcdtt8+p+663Ft75V3HjjvDN5lAgkAolAIjCMEMj3uw2jzsqqDnUETjihuPTS+kpecEFx4YWlkSmlvQg8/HBx8cUFR+dqq5UGvHzhWnvhbUtu06cXL7xQiIXP3mkLnpnJoCCQPGlQYM9CRyYCTz9deKNFnXDGNZ6sS5Nfe4UA7fu735Uuzp/+tAwY2mKLYvfdy/ihJZfsVTaZeAAR4GsWpXfddcVjjxXLLFM+J2y4YbKlAQQ8sx44BJInDRy2mfNCh4D3pIr5rQvcttHO2KHxOzv7Ly+ec97o2UXpbR9bzFp6yecP+8Lk4dhJp51WHHJIGSMfcu21hc8DD5QbHQ0RqIcjqu2tM1PfL35R3HVX+YsgU6YU++1X7LhjYbVgSiIwvBAYGvP38MIsa5sIdIHAO95RnHNOucytVrbeuvAZdPn72bM+9YmOG26vKjJq4tixL8586vNfXqI6NVwODj98Hkmq6kwrH3NM8qQKj8E8YEM6+eSSJE2eXPYI29I99xR//Wv58uDVVx/MimXZiUAfEMg47j6AlrckAs0RsKcOFb7SSvOurrJKcdBBpWNocOWyS6Z96APP3XC756LqJz962syJl108fnAr1ofSjz++uPvu5vcJok8ZCghY0KCPkKTx44vRo8u/PnfcUdr8UhKBYYdANWkOu5pnhROBIYcAlfD61xfWuP3618Ub3lCaN+xPuPfepZIYXHn26bH33s/hMWr+aoyaVkyc/8ww+HbKKV1W8qSTuryUFxYkAlzP4sZ4nCtxLOLeyZREYNghkDxp2HVZVnhIIyAaw2P0vvsWp59eWpIcD5mAjBqtNaQh7KZye+3VZQLWu5ShgADDqje9PPFESYx80Cah9y99abHyykOhdlmHRKB3CCRP6h1emToRGI4ILLZYx6rLz26oece4YkHvgEllPvdc8eSTxfPP99G68O53F8su29AUtrJRZXBYylBAwMLDt7+9ZEUPPVT897/Fo48Wyy9fvO51TV6TNxRqm3VIBFojkHHcrfHJq4nASEBgu1eO+5+vjfrQ/ljRvJ/8xDGzttt6RlEsuAVIzzxTbrl51VWl4rTp0bbbFuuv35fI6+OOK448srjyynlds9RSxZ57FostNu9MHg0uAq95TbH00iVzfeSRkiS96lXl1gC5GnFwOyVL7xsC8ybNvt2fdyUCicCwQGC9dWd+9OCOs8/suO2usr4dxezPH178z1cnLbDK22/wvPPKteL2hySiVc44o/j854uttup1FfbYoyRYNk+qNqbaYIPiPe9JNdxrJAfuBua9zTcv39vDgsgZXRurNHCFZs6JwEAgkDxpIFDNPBOBIYfADq+cuMMri7/+efqtN3eIVJpezP7sZxddkLX8z3+KU08trQvMDOwKKM599xW//GWx0Ua93n5QvPx66xU/+MGCrH6W1RcE0KO0IfUFuLxnKCGQPGko9UbWJREYYAR2e9OE3d40wGU0y14wr6hea8W9vyIUp/B2USw33zzHB9fspjyXCCQCicDgI5Bx3IPfB1mDRGBhQIARiNSuDOd6S3vDwtD12cZEYFgjkDxpWHdfVj4RGB4I4ENe8vWyl5VvRbU7M+F3sxLKDpyCfFMSgUQgERiyCKTfbch2TVYsERhRCFgl7r0u999fvs4iYnvtsnPooRm/MqJ6ORuTCIw8BJInjbw+zRYlAkMRAYuedtihfL3XRReV2+qsuWax887FEsPv5XJDEdvWdcJKf/e78o06YA9h1Zs4/HZib93KvJoIDBQCyZMGCtnMNxFIBOoQEKKEJ1nAn7IgEUCS9ttvvgLZ9m64oXjJS+Y7mV8SgUSgKQIZn9QUljyZCCQCbUBgxowyIEm8dspgIWCZ4e231xfu5NveVn9y6Hy//PLissvK9ySmJAJDAYG0Jw2FXsg6JALDBoFY4X/bbcXTT5eL/Ndaq1hhhXIjwTrxWhK7AHhFvLeUeO3aOuvku73qEFpAX6+9tvjGNxZQWf0v5txzy/0jxLEZZrZr//Sny9j/tdfuf8aZQyLQdwSSJ/Udu7wzEVgIEXjggeJnPyvOPLOMyLZjpPdRfOADpTLjU7PR9mOPFYsuWkYdeanIiScW99xThmwLhZHMu4E53VISga4QQJKMpXvvnXOdVYnR6ytfKT75yWLChK5uyvOJwIAjkDxpwCHOAhKBEYMADxqSdNRR5eO+pf7PPlv8+telYemHPywuuKD0laBKkyYVyy1X3HRTgVF54RpT07Rpxd/+Vr68dv/9c3XbiBkL7W/ICSfMI0mRO5J90knFK15RflISgcFCIHnSYCGf5SYCww8BwUbHH19ugMRohCcRHOgvfyle/vLiT38qRCN57qfbWJVQqJe+dM6iKltvI1j/+EfBW8fs5J21iFSjq274wTEcauwNa7xX3/nOMKirsdQo//pXaZVMntSITJ5ZYAgkT1pgUGdBicCwR+DJJ0sOhOIESdIex0KRfvvbct/IlVYqaRBK9PjjxVNPlRHc48eXH+I8nvTFLxbTpxeLL1688pXF3nuXhGlARW3VhHOQH7Cq8ICWOAQzt73n5z5Xwv6//zuvdgBhvElJBBKBniCQPKknKGWaRGDhRYCLzYO+v6K2cSAfBMgZpiMfCpgZ6cYbi8mTy6+0MkokIAlt8u42Z7jerD9/8MHyLqSKhhao+4tflDcedNAcFtV2cLE0Vq7/9//K1+6q2BvfWLz//QPl8vv3v4tjjy0uuaTYddfikENKWjbUxHv0vvWt4stfnlcvrFFHpCQCiUBPEEie1BOUMk0isFAggAyJzj711OK668rXibz+9eUitTPOKM46qwxC2nDDcgmbt9hKxvXmYy0b2iQgCQGS4JZbyrtwI+wEeXKScMD95z8lx2JtWnHF8oxjbyxRyjvfWa6Va7sgZMLMf/CDMmMk6dFHi6OPLutz8MF9tyrhhSLTf//7MgCLD8h2RMiHtovKghJSSIRnff3rJWUcguuzYO4zxGXLLUt2+8wz81XzU58q9thjvjP5JRFYwAgkT1rAgGdxicDQRYCO/9KXysX8IoqEGVl/xGuG6IhGQnr++teCJ8vLRgRoswnhSQiTBBa70W1Tp5a0CZGS0iVaGQdCILAWHzTFSXdxumFa/vLfWdk0EDxJJfEk1RAFRSKUCsXh6RNL3gfROuHqX/ta6UzEjehyfkZxWhyOVHiQpMhWM8UDaSYAUyAAsTA9IqwMja3Fujaj6MILy1EEW7estlqJJ+epjxGFhXeViTEJfEMrmHrrgvJqItArBJIn9QquTJwIjEwEkBgqjfoXM0s50UYIgddc2OuPdQThQInuu6/kBAxODEiEAYnvjNBqlBNBrSIMCEdx7C8axLZE50npRtqOSclJZxQR+ZR3tlUoZuqWWasSPiZ6lIOsDzxJw+3TaEEffLQu2OE//1lstFHZQM2sE2TRgnYBWAu5wMHg+b//K2zgxBK5zz7lHlrd4s/yR370o/Iu8PKcujFkm23KECsrBuqES9fItMk7fr/qquU+Agx+3oqTkgi0C4HkSe1CMvNJBIYlAvgQzxTFf801pQEJD/BcLsDIeeSGKYg9yaO8Y8TIVXYgKtBB2FHQHbzEq22dodiIZ3r3+uAldJirzqBESIZM8BUZOol7XX996b3i2muvsOXQxwpCcYK3aYUKiJ3qg2imV3yEuUKGtdJIkuKqWKWFnCcB6pRTyn2PDC3CROSrZXeixPR4tyLMC/neYINywFRyxRXl7fqiTo47rvjmN8tBSxCm972veMMbiu9+tzR8piQCbUEgeVJbYMxMEoEhigBdzr2FsjgQT22nx7o4FZrMhpB8SbxFTD70jY/nct6rCDNyEjeisbifgihIILfaYyTJV6QkVreFegujVJAJS9vQFyYZmWAtrEq+MtIw1Rx6aI90Z8/xZT2yraWXmiF2SkF0ELvddy8b1QcJviiTaG9nDvPTpTmZdm6T0Hm8yy59KGfeLRBjyYsdz+G2/voD4p2cV94AHNk9S3xYkKTIXozar35VbL55sdNO3ZcHgT/8YT6SFPfwCxvM7J2VXHxxycCCJFUnDWavWE6eVAGSB/1EIHlSPwHM2xOBIY2AR3nOC0/n9L33h+y2W0GLW4OGyjD2EM4ResVXBh58whM5YoQToE28Y0xBbD/IDQmiEL6n8nunhOlo7reSKlWUItJjS0QO4oTQKSKge8qUkq7JX5A4f4qVYm0UpiPNVG1B1hgeA4b83/3uOeau3haEaXk3i2bOlaYkae7Fzv/9WfKGIlgniLkKpdcdegq3+OAHS8SGkZx9dnHnnWV9jYcQg0GYv6HVEwHCMcc0TyhnaFTy5z83sTApyyBMSQTahUDypHYhmfkkAkMOAd4iS9Ypp4gE8pTvsZ6fi/ZCUDya0yixyZCYIV4zydAa6pkfzVeGGQHXgoqq5/VK7VVNdSb4kDMO8K346rwcHAeRkgNTE/3njMghHwUhATK3kWB7eZKcmY68JoVRQVvwM02OmO6q2q0P2C0YP0Kpi6rBunoltiy3Dr9vAhC6H8Pj+oQStofFQtLWU/4OF0GLqzETdTYe6ih1i7boQY5LsU2NUvfqm2DejcnyTCLQRgSSJ7URzMwqERhaCJx/fum+YUZiFiL0Lp7EoyFYG0nCgTx2MyDhLigREmPRO2FBCULD3kO3+RtCe5Eay0r5NVhRedQptV8dRz6u0GcUpzP0pVLQF6Ww9EiAELRdUArRSGHXUWKvBD7f/nYZsKVuBL1zZq60MCa5NKck6UXM9LbcKIIB7NJL50R0xRnVYBRE2uoowtwqDcX/bJb2sRRYXYmuFzRmyPVEdJ/YpqY8aYcd5stAhDgGXOvgm+9yfkkE2oFAdys121FG5pEIJAKDgoAwF/opSJIKhEHiootKBcaAxJyDqVAzdjPiE+GAEz+EvkiPYeBDAonYhyp933N7gLKUG0FLjuXgg5PJ2XmlsJogBEoUQrTppgOFTZTb89w10B5IsdgqSJJ70TtBVD2QeXTMaruPfKQHdzRLAnCYVF0mCR6JVjo5jGTrrYvtty87XXfHx7GVazvu2KQR551XxmDhtT5efKuxEjPj1W6MGbc5+ctfzmHzccYyt223rc8Tedp44/qT+T0R6DMCyZP6DF3emAgMdQTWWKNUOZVvgu5HBWhcDCmMQ7QyyoLBeCJHXKgo1iOxw65SbyRu6Wc7cS+iGj4qIGfZ+osNoG5MXENEmNmQJKvV+imads459Ya3HubJ4sJpyN0W+PvLMWrbhb4Fofew0LYn060clx//eMnC+T2F7bMPcR3WRW5pnUDs17ymZMzGoY+3BGqscWKIfuYzpUkplkMaQvancFIHffaz5fLDECbS004rdtqp5PfxEb59xBHFa1/b9jZlhgsvAul3W3j7PlteiwAdWQkry0C/eqwqa0APBOicfHJpDtEchEnUCB8cVsSAREX54EY+QqpFJgVJUh9Kmm52NY77X0MmGaUHM5ObA/UJv5iCrPdmRRh0Qd2MgauumtPwuvqgLz4PPsho1IF5ji9esPvB88Ui44qZLxbjO4r6bSVBLe6qD6Yydj4bfHuvizh6/aJTwGXjcmNy2Mn3vlf4tBArCbxSpk4wdYzqG98oDWnveEc5MmWCeEWMHQolesyWp6K5DaoQ/uWURGDgEEh70sBhmzkPGwSuvrp8uX318SA7dIwc/QGRcv3858vndQfU/FZblY/1NpjxFE4Bc6shSfiBEBzxST60EQsTHe+gUkJVBRrPVJe6PcC6FETJycTHV5YGVWJSqvUxdZvPwCVQPQ1HSprWB5P7+c/L7QYmTRq1VPH4NsXVry3OW6Z4YmIxraNoMotqIHj7IEwmwsY/9KFyl3NUcpNNio9+tNxJnDDFqWTw1z7k3P9bFI3yapdqtEXkwwnbKNUre5VlSwvM3ogNMTJ9LAlUk5REYMEgkPakBYNzljJ0Efj734s3v3m+6tGIlKWHWivYh7vYrE9DWEroJMYkjg/bPNoYyZaSopcQFy1FXJyRIJxicbJRHzee6S04wbQUxMMiMJk7Bi9pjC/pbbZtSQ8HHh978zDkoI+1gtIJH2bmQTf5kq6+eoWLOlbomAPHqKB9tekdw7lx5+i6NF19Fe/MgCQUmrFN0Wxv+kU8voh75EAlbTatJgFmV5m0/byirZe02p+50UZcxpW6oSwDIUZgiOGBITUizNK2gJs/EM3MPIcLAsmThktPZT0HCgHhogwJdfKTn5TkaQTwJOqEicJLNirx2ixr5u3KQ+fZg9s2fWxLhFXJg3uoKByACkRo+ilKDyUXpEKeNJ9SCJ8LFYgQDMrW1ZopmF3zqX+BL8J+MQ/8Q6AxU2IY1aLtyJy328bbM3gwLdZjeHP7M8/M9fo0YKTJ4rib2qUa0jY/ASgOOJ8QfqWTTiqpkh7Bk2yj8Ja3lDRFQQtGlGt7AkvYIjDIEAKULalETfWnDgYD5xriVSfVc4sRInabXY2nmPVRWcaqk96Ya7imJAILBoHkSQsG5ywlEWgbAjgH7U510TE0TR8Ec2LFsSiJW4cLw/vIKGbkAIOJDBVBG1U7AvShiLgl6FF1Oz0nWwpebC9uStd+4AP1sb1V4oE4ABpzEV+PtfeYh7+UNA8XhxpDEV7Cw0UrX3llGdhuHT42iSuzJMGHsDO517GPG4PwOR+USObQExOGQOB/7RLUxKaLjH8CdJADFVNzFh1saYFxBSv4bMTFEmnkRNu9aXjKlPJZgmmnz8LqdsQRZVx2rRgbTobA0xDda68yvjsW/7u6557ly3EZRNUKYzOMGUqjg3SBPmJ+Y4Tz60hJBNqCQPKktsCYmSQCCwgBvhhLhBhCPFizf1jXI6yqb8/0lBAFw6MkMBZroWOsGGIpQcLCAdeWJlVUiSaj3alGuk3RzDY0XGUyaUtZrTNR4o9/XO6AgOtocmxH7havXPXWMFWy6RFIGWxe97qSA6mn41DAkTNLElqAISFGzBt4A6yIRllmJYbJ+nYmlva+MUNoOcufngo2hmhykmJy3vZa8STVCBrBZydB2+Uf/yjhQoygRNQEHbFjOFLYH56E9BxwQFnhI48sx4Nh7PUy3mNTCyDGg6e6hNQScd/6SACTXwHbnhzQXBwX75cDOmXpnNErjazWW2++7ivvT0kEeo9A8qTeY5Z3JAKDhADFzE3G/cHSQ3NQFRaLffWrpRIVvEL3IzoCRyge2kIaeoJKQ6pwIP4Ln0qzRgtQASYTygZPYq7ACdxF6Qa5UUTFcvrfYioWz2BMItwozDZ1ewbWFoGuoSOqpA5tEbznoIOKv/2tNJs1NkqTbYGNCkCSwFlV60iS82GB4wWDMKhh64MrsELZs4duhmS7Kly1WiwXxa+gEDD6oMv6lLhk8/Tjjy97nOjxd72r9CTW0rvyQv/EqFBQ1TQHPjrIyX4Kd6egdZxGB8kT7BydtcL+Z4Py00+fQwTDpoUPGSEw8bTAsoUj/vGP5RI5nDJClyw2RC4PO6zslJREoJ8IzP3x9TObvD0RGFkIHH106YsZUkK7U+Te/EU/MWBQKjSlB31eGB4i78qgUJ2kwulIC4Wk95U68deHBqJB3/rWMhCnEpqG/YOdwLM4ReWDRkhMQjG7nVDM/deIMsfk2CGQMySJDaDpnkCMBNaBW/itmZTcgQeWfpaoUlmVvoqIFkajRoZU5WdbKZrVztd26OHqgqGgJcMAsePBiQrgoJ/4RGmOuv76snpMSuKExLex4gR9qXJr4wEblb7QoWG50en6AjI6mqARX/pSSZKgqnWagDZ95ztzth1qVzXEtwEBR9FkoivVgdUHL2wUY0bEt5EpjZf3Aa1192HDiF1T0Vg9ggVqmnHrLzexwDLntTdEKTZPDwsTJyn+SgxjVIlD2fZL/bF4zSkj/y3cCCRPWrj7P1tfFPan9qJT7/Go5CtfKQ45pPo2VA7oBkqCISG0smpRnxSVt2TQH1SRB3GajMqkwkV10PQ8Su5iY3AJQfHSMf4aUUHWUjGWhPaibyhdq8+CJElPaOXQhdF4mobyo3v6KfJk91L0D39Y7LRTqUdVlSAZauvDzmTnAns+OaN6amWlHpuBMOrWurZ1xVBeWrO1jB/XcdONxVe+Wu7DOX78KDiIXLYWEkWg6cUbbbddaY2zyYKaC47BWfl6kJiBFkYsLjZ0AUdBAlSP0evVr57DUdQQKan4gZ7iWLRVppgeeLZLbAT1pjcVp55a2h3VwfCAiYEUtKm2FAnYO3/725JN6l8cyKKBD3+4HKtGKelJPxpshqsfpgHDROQrkmRIECNZV9aaRR0rlGFVl1XnsTonxd0btMmTSuBS+oFA8qR+gJe3jhQEPIUPfaFg4lmZ8iDUBv2BWIhWcclzM73oGJEicZKiop+oHKoC52Bw4sLwkM0oJRYn3u3ANIVI0bUol3slrvJ3O5E5ptJGUTdMlKuLJQaTI9Tw4YeXYSioierRdqpNHKAjqIA19uJUfKWYe6v+GagE/HYnHffc9OyvP//YC08tu/jYWR2jJzz94sRZs0bxN8HN7f/v/xU2N8cvw8UmgGmBifZ+7Wvl9taQ0TU2wXr720uaErwBLcY/YmCokgP9hR8EKWlXJZVlIy7jxDtGUFvsUAQ3N66yasWAwVfQXN0HLoLVsXd6DlFt3jEjFsvXlWEMq723Osb1edDszVGNOgVVRlA10UYFxbOBv5iQgaEINtE4KatIEFHnVc55kAj0DYHkSX3DLe9KBBY0AnSSqGcPzbQg3uNJnc8F56BHcQ72MEqInpDAX7vzhWPCVYoN26B1qBNkCNXgmqF6f/3r8it/HAMJdesxHSdwL5E/TYYlUMzKlWej0FgSR/rGq63PMHi84Q3z7sWW7KbI3calQtupcEXR1JnxhhVKQ+hL6pkVpycWAmgwaWg1j6TmtxZNnDD7+dueWnpGMXbWzFFjZ06fVb7UdkJHxyg0K1ZOqYOwGOB/6lPleque1KF1oT2/iiWIbt5vv5ITOI76xO08mNFB8CHRU+GW7Xn+PUmJnTCqoWiA1Sm1DAlZR25UzEkDCeYGVSRwwInJccaRyllpOOFPuCbG2RWAGNJRR5W3a6ay5AZzox27IhDQZCcNTiJDaTwkMHfZNwHRl5JIYJyj112VUiZKSQR6hkDypJ7hlKkSgQWFAFWH/dANdI9Z3oOyZWK0IMUggthJOoBliHrwQXSkwZN8KjZDx6AvHuWdkYa+CULjq5M0jQOrh8TGCh+hfhiW7Pfzu9+VPEBuEnCsEOrQB2upcq7FQJ6oUtNLtcmaHjeyK1RGiDrmF/ov7pJMZbA35AlvU2eun+9/v/v3nMjEaiy+Knwx1HPTasw96VUkmOCokiQV6AaqOG5SMe2Z8oUko2hi4Cs9cGDckjlfEp7X6HWam2H7/4M6iEJd1lxy+hE/CEKg43imUEnp2y74kA/CxCbEVGOYGYf8YkLg7767dPUaSIxGzkfpQXaNQJYw5MYtKomj/+AHJePnT2wUw+n3vy8HFZYvnzArGvCylaczeoHd0bFXu0T+MjE8FIHG6Z3Y4UkN7Zuw8UZzcmgsKM8kAj1HIHlSz7HKlInAgCNg6qdI7HJ51lklW6IaBcRYO00JCYLGJCgAKod+og9wI1qcUnFQyzwcO+9vnHQc+iYUmJPS4x+2T2RJEo1rp0eelD32mBN7q5EsMQiZv27xkUOjyKc1SXJjSFSjMYe6M5YsdSVgwdjwFW5BPjturxYchWIW2CsEm79MK+w81G0F+Cf/WyzdGa0+BlVCm8bV3EQ3QyCg0GQRyvIEDsXMXjK4ouO86cy7YDFCYpMI/chBVoHfluppNYca3ilcDGW0FF+AlDApJkmGSeyW6L7YsVPRzhhg+A0xVtEjg1YPEn9VFSkH3ZQp89nGXIWzew3Xqv6OfdyFDGGBHhvsuXX22TXd0/mK5XDVMfVdftGMq0+auu2MO7a+//nbj19h9lobbrPb0mN0aUoi0FcEkif1Fbm8LxEYAAQwGCTJszJBCzh9+CxEpApPYSCh9T2IMx4EQ6L+cQLSyAOqR21XHUtPWAKIY4qfIGQOaH0OC5G2ormtm7MoDBWjC+k5KR14sldubYblzc2EbquriSaQqGSzO3p6LprApUJfUtXcc7Yz6ErAxa4gGQMGpUv1uisQ6OoW4V5YpZXvEWrcUYydXhqW5tlkEIVQ2NoCGQzsu98tM7edD9IwiKJPLarnkUTdiIBuTCJsS22slVGhvZhKDCEuWsUxc9p8EgiV6Bc9xaCFCUU0kktsP/yAQakRHUPaA8AZZ5RmOXYvrC62iohM5I9+ud1ACqoUN2Kl+JbhKv/f/KZ8iqgSxI2+crCeecaLN//q6jfccMIK02+dfua0p4qlH33pdk8+/qHd9+3cjqKqaB4kAr1BIHlSb9DKtInAACPAL8b5ZdLnLKMnqA2qxa4/FrWJcaaoaAtnqOqw8fSKgkhMOdE08idy8MRPD7G+8J5YISVwhP9LKfLn7VI6XchyI02o4ar1jZTIpci2SuOrEqX0ofwi+Alz8hJ47htRR6ECq/StDySu8sd7WghtigIqmj4Oq1t1Y4u7+N2o5poEtcflaRnqHaIJOBOU9JSGfOxjZesGUbAiDq94NQ2o2y7QE8YOUoNBw33Fm0WYQQOhqS3RJZgTVJUg2bDScQYtegQloBlgRhfzkgzjTSzsYcGn3eKA9VS8mpEZZcVQFxwW4vaf/nS+Qudc6NyU6/kHnxhz3G9fOvufTxeLP1dMmlg8t8Hdp5z+uZV23/d9VbI8SAR6i0DypN4ilun7i0Box2pm7G92I+t+z83MRRGKES2jLegMQTkoDoYUj+8V1+lV66mx+MRdeBIFJivFWQGHFXGmfPvb5Zomz+u04JQpJUlylZ2Jagzlx11imb1IqfDKyUqelYTWrD3jWD4y1O/K0u+ciaw7lvrbJ7PnEjYwudnRkfmkhaiwsC32DHVWkFtQnJaCXGiDj4NKao/nnKxon4ZgJ6Cg0W3jObg8KSpXy1eqNrTlQKsRGpAaikRB2m6UokTxW65KcckQ5RslRk78xuFv0OI3RKfgPbISec3aZ0Tx5Rl1tfsnMdExqQrqj/BtZbmrTuRJ4m91aZ21Z5/09bu2mn2DPVPtNtV5vmPR4vmXP3jKo/9573Irdto2q9R5kAj0GIHkST2GKhP2DwGTmqdJ+ljEpQnX5iv2POTiSalFACD0B47iWTyElqKNaBdsg9BYdeohkvXwLwVfK3Km7ag3pXjiF4PyhS/MeZUE80+QJCyNkkOq+Ef4dOg8tqWw6KiJe31qqxTaUYYhrrJGNDIb5jG77HhhhTpgNgw/tZnUVrI6RpWY2ewD2dqexFFooZNwGXUIg1yVQ9cHdayo7uu8+1RS76jGaquVf6ERgcPzUoy4I79WMOp0zDjWnQFBXzAvoUTouy4m0X02xwILZmMYxHmX3O5GvWwY88Gh3Qa5qwgTMGVSy5MQIzPDbruViaWx/M2eEd2KIXH0UR3H7PnsduVQnbsHZTHKrt1LeRx4bHbypG4xzARdITDPAd9VijyfCPQQARMlnecTM2bdXTTKZz5T/PKXZeCIJ0Wv9qQjnUypRYCO8ZowQg/hNP7SFnQA3R/HtYn7f0ylBHOi87hCPNyLCEbRrEWiw9hjdCXXEgVJ7Yl9kUb0iTAU6i1Egqq7XaLYfJUDdcjcIhNkS+RTozh/8MGl44YDThsp0Z4IENShtVC0bE4CuRiTwp7UOv3cq7R99Zl7rov/ssWTtBQsTZeh1d7nF6GBau4jssc2jJps5Ds/NEW7jIroVn+vvrr0kXnIsfeEXy4vrSWEOi4YtiZIE4kdY0jGhtur1jkwHvbbr4ys96YagwFixonRxRR3zjmlt9euB7UPAJiZAY+Xe2yIdf7dAvU//1OssNLoXQ+eYs9whirD0C2jitmTimeuLzZb92WdoXnd5pIJEoFmCPRscmp2Z55LBGoRYIc363mFgoUnLEY0azV1RjKvYaIk6GPqltXdDHjBBeWZ0NO1WS20x+FW82pby/URJtoiCAdALHa77LJyG6GBEF0Quo3Wp7eI53sBtuwxtCONLgHdxlTgQAgzfR92BdouxIFutfaK5UAa9Evvx7GAa5SoK5FYfDqbRJCqrpI5X5VFI7YWillz1MEt8h8IiTYa84DaYosuS1ATZhjL4MUPRQOtW3z3u8vdg5zhS2VKqfuZdJnXArmgXarEmWhBmd8mGPEeqwqMzDXWKFkv0Wr+Mj/kKVPKxZJGaa2APQKuDRu3M0b6i0ALn0Jw2flQTN1tfjBLKCtKPO64QrciYY1irvDpVo44olh1tVGX3LXi2aP3GFN0LFk8Mal4doXi4f8WK9+783u7vT0TJAItEJh/jLdI2NZLL7744iOPPPLoo49OmzZtlVVWWWmllcaYocs9xKZPnTr16aefHjdu3Ete8pJllllmdOdj5gsvvHDPPfc8++yzEydOlH6ppZYaVc2aba1YZtY3BDxZMg5ZJEyDmviEF3h8tHOgZ/pKrKsilceEojVXWkfT4mWo1b0LwwHdY7Nsm8cAk+6hUbgn0BQ70/gRUE40EBkItSpP7Idi4w2xWyDtrnf23780mbAl6Dj8LNxM1B4tqHo4E2KEBHDJOYkh0Zp4lWTshQ6oQ9W2QJ0RkYQ1Be3D/+p+uwiEX79ClW7wML00trH2lsarZQE1wgBmV0PKWHGqMUCi7ZZr2Rqg0aUYJWqRXc69K8aG1NpVJ9yatiOHs62Y/B0igql4UZoe14PYLT5nEDId6W6s18/Z4GRVUmG8md1Ob2LwRAfpRH/dGI11rNND2IpsUGlLbkFpgtv86pEkHe12nW6cOEbQrSTAxqopIu7lgGO+AmOM/zk5NvwzKozMr3xv8ZVGvWvssovvMPbKWY8/O3PdlW9cZ/dDj9pCBWJUs1EF22vIIE8kAl0iMDg86cknnzzttNMuueSSiy+++NBDDz344IMXWWSRmTNn+vqLX/wCB8KfNt100wMOOGCFTk17+umnn3DCCYstttiMGTO23377ffbZZ7nataRdti4vLCAExIJQ8CYyE6gJi8q3P4pnTdNiJwEuq8GQTlwNnWcydTB0NERZuUEVS9k5Jgj9ZE4XLQQivgnEhUKiA7rlB/2pvrLwJMXRWzQfswfHGRJg/2umBSuMWAWoPX8pQppGrfSdcCXHFKe71Flfe/SXwK9TaDOa4p1oDAn0ojwRCxYFOSMWla5yS6zVYlWiL513F+Jl+0SZ050IXK0YMwZVC4GShvANmTnUSp3p4LaLquom+Aizi/HcWARq6w0etrwK3tCYQDNtEWl/LL+UoSDqw8SFGeNAyAobEuLOg4lx6hfN1GR4ag5I/cadhIN+J/oR8o5jlCJVcpBhrVhTKWcmZ3tn60RjSYaBnvSKk4Axtc6vajzgasZbYzR3bebV8X+K5e7bZd+Zr3vjI1Of3/K1y6606KK/+s0otEwrbC5l80lh48Z2SiLQcwQGhydhRdtss83LX/5yfKiqK3PRcccdt8EGG+y777733nvvkUceudFGG73xjW9Eqr71rW/tv//+u+2227XXXvurX/1qww03fPWrXx0mqOr2PBgsBNAjhnrGoZhMTXymNrqKavS3itSmUWhcyoN+NdWaQz2kcsqYalP8DoCDYTDShIDRczzlQUNAuCtd2y7oqDcler6n6uxDw1VEdTmDotmFUj/a8AbRUQ3dtwBS7wAAQABJREFUp0o+xFdpOJ7UE5NTT0yLfWinnUrCxBgmkol9wuvzWKTCQSM85f3vL7bfvhwkpUyfvuwSYw49dKzobHRKDkqkLD/3uXJfIoHedg5UemhfZb3rXeV2Qa2FDYkip4PVxLBU7faKarCTIQr2B8fnqieBulKYXuwe3pqlsXOo4RARneWHjKboAmIAsMzh6xqI06A4mqwftciHmRPzWHXVsluNmYohRVv8qF1tFMseP/7xkqyg0SxqkKwVRcQgqT1pGrH4o4ckyY2GyvhJY3Z6y3JYsrsYomxhoC1ylpWge8d29K6zWtWWmMeJQB0Cg8OTWIY279wnjgetqtBDDz109913H3744WusscZqq63217/+9dJLL91ll12uvPJKfre99957yU45//zzb7jhhm233VYmcW9HRwdb1Cy/1NK8Py0OqmzzYKARoCyD7lazXhxQmbVKgjblYrD0idoj9KiAZaaF6q6BrudQzh+AdNIc6tBZ0aAplChmSfEjEAMqeqHiIldeWZKeymfqN8qqhLWwG+lrYSu8WmoVtgRcxIY3RFwaHxz1oxVy0PUypGJpXyqTV45VADlmrqB0vdN+xyn/Xv/Jy8c8cO8iiy6629qbrfL5TW57YDGaWLlMUEws8uesobNFtlF1zr/lLWVNINNCNESFWQ6Ui/apgCq10fumdINWW0DEH4Qgzp2H5quUElnIML/WwrBR2+mtEw/0VT9MvVZbH2ACkOdL4LlYIh5Vf0P0lG7VTGlIDJ451zr3Mq07U12K88iukQC9KM4BGyTHXCOYxtJFF1V39+hAEVGKDsL8WLKNBOJBzg9KhgqqHkh6lGMmWrgRGBye1BTz++67b+zYscKSXGUrWnHFFa+55hoE6NZbb+V9w5GcX3zxxXncMCqRTBVPeuyxxxiZ0CkJpL/55pub5p8nBwgBmkNUisc15pB4HHRAyQlxrTWhs0NQe14gykxi8qU4OVZi/hqgig2jbIHj0dw7ROn4UDw0ULBPB/DsSuu0pY06K/JHbT1woyZ0GE0WNVEEMrTllsWmm5alcaVhCWLwEbtwZHCcoTIUp/MaotrCXPwVtISjqDyJ7XOoQ8qYnWnsXbdtMPFnq8y8aslJ5eVJK5637dvetuWee8ycsKhbDI8QWhOZZnxyi1opTv7dilGHUVlQyZJElwev6kqjd5tbYwKqHTIoI9yqqtYlo4/FJnfba5xuQ2Hvpag8U59ZVgfFz1YDPXv6FeOmggg5hcUt6R3Ndx5x92Ezq30WqkBo3fAgvm6MRywdxLRsR3gxeY14KiWGUJV5HOy8c3mL9bM2mKgTfCjGCWOYIVeNGZmboIxb4zwlEeg5AkOIJ6E+IpPG+yF2Cs6E9Dh0Xlh3nJQAhXJ+tl/YXJk8efKee+65I79Opz3p9qarJuYmzv9tR4C2sLLXknJBIY5D9AZ/TS0NolqoW2ZE1gJzn5XGok8kMDlWvrm21224ZGiA00biM/g1oGTgm8rN6QgoBR/OjoFrS/yYdBD1hlhgJP/3f8UGG5S8pFaCBHsQ191sDCrmx0p3cm3YmpIbhU6i9eUjE6YjFh20zzG16pKPgmQycULHjo/9dfnnLrlz/OIbbLzYIhNnj2KmOP30ceutNw6Pnl8CBDj0XAwqSpSpxgCjklE0tgpPT0pvrb+7LULTtEWGTBQ6SBx37QivblcK00tPrCDMZiBqi4BXUKC33JD3vKd0bjZyjtYFeUQVNsTrGnZBPAZhEsoNfOHVxoNfN2+mvxroo0RoBKSQIXHcohQjSlS7cc6gGIkNdXMFN6ueN/IbRbkIOuNcrSgObsGHHNeWi3LJKiYixzpIJeOrAaA4N1bMqTbPPE4EukJgCPGkZZddVpj2U089FaajZ555hvUIMVp++eWFKEUD0CM+OJakijk5P2HChLU99XSKq2hTHOffBYYA6uMFZJa8sc9T+eIPPOqJAG0UExYNKuBAiCv/C7Fm+LDDyjiPrsQkKM7AFoimb1Yoa+bNpzEvd3XLcDyvXd/8Zvl8TKOzowjx0WrUk2UieMzANSo0TSgbylXHoThcXXU8KSogMTUTmkaneNkWf5yudKOq0p0OfI1ei7ASne4urQjbwJKzn1zx+duf7pj41LQJ1/5z1OTJYxYZM2HUbfde89ydo9+1GbuUsTF1aqnPvOGEU68pF2mBhrKocyYQ7hVvzP3hD8t1Z6wgTFxYl6tNTSAtMnTJXbRy2PYYJNhR2NXQEeebijQ9lFod38NbGpMZJx/6UOkHhDDRL15Py3DbK9Fx2JXfI/z91jjK4c+OGI+uyAr05B/mNFBgwFXlHXQFRVUH+bzvfeX2FmHxj/Ru1Dssf9bBNRX9iPVqYFWWZLLyoCUk3HjzlIVmx1UjTf29T0bbWSIRJk5SQdyIsmobn2Yno6LyKTctMU8mAnUIDBpPElRUJ2uuuSb2c/3116+66qrozh133CGmm3lphx12+OIXv3jTTTetv/76U6dOvfPOO9/0pjdNmv+JAJ2Khjmojuuaml8HDgEzrMhWVKlb8TznOdtTrznXjGZ2YywR2sJJwfHUKJQrb9QRR5Rzq+dCd5m+vTHeM657TXxeoOH1F+ZHxMKDKQUZ4mpFL1ydO0AaSxgqZ2DIwUT7apRmmtaZB7SODvDVVc0fCIGM/CvDAA7qt4Ws0JQtBLxoBw2EJCE0jCtuUWd/sRBXCdijC/xVir/O+4ydbhvAaS90jLLDkUzw5rGzRi89fvat18z82dVzVvapEpMMDUrhoY9y7q1AjGGMllU9GzqLJddGdTBCHBiHPZGqCQ7kphpaOmVKOWJbxAJrLEORxFrXWgKo1mm6vYorILV2L6sEm9Ev2u58r0Sd2TV9qoppSwgLIke5wHNQAFBHV2kiQd3XunKRXT9bGHbGR8z7PcqfU7Xa0zwyqQqViX7Eh3jQTBquuqQH/fyNEAxJhzpgpRbyb9WtaYRgsageS6cSbcv0ne+U4HjWDlOopzh5piQCPUdgcHgSuxEaNHXq1H93PvKcddZZYrfXWWed3XffXaQRRxtWdP/993/yk5+0YdK6664rmvsrX/nKO9/5ziuuuIJJaeONN661J/W8tZly0BHwnC2y0lQbNiGzHnpklrR0iDenUTyzmvsoOQ4dz4KeYlngnbH3DKOFGdA7Ys2VBKWw/4otnuXsLs/WXIEmRxqLB5BuGxaTI0BiKZkWqTPHgUkfXM4PkNA9YYRQhIIg7KuDrlxd0lPMlg6xHQoNEc5C+Xm+R48qA0PUNnReVLv2+MGOFe8s1ppS3Mw2MXvmmFEdHRNHTXti7EoPTV5j+iNlBViEw4aBZ+hWYeMUYVAcnWsY9FxodDtW8L4FMVKNsGL2MAdjJkaXGx1AhjuP6m1BkiJnySzNYx1sLaxxmmavBID3WTxpeBdvo/Bt9U10AUMgR6om+90ZhPiTGv7618UnPlEGn2E2ANHLOIo0QO5WdCWzsQi2ptxRJsaPrvFUAHPmH2MgwPeYZFsKjJnR2hk/Z3THqJAmRizjEE6PGCFSKH4EV6kSG5WRc8gh5Ytu0Dt0PNoSg7PbCmeCRKBCYHB4EnORtWy2UEKDHn744Z///Oc77bSTDZMOPPDAX//616eccgrX2xe+8AXMKYxD9gU49thjTzzxRDtSIk/rsV2kDE8EaBoTljm3UgwxbXWlvcybYoo9QYZ2pD75pBiQZMKYYZlxFW0gB3vVxGY8Evz4x6U6J9bbI0yWl5ujPQQTuclk6E+XZnwzuydp/IMZY+CkgsIBjYWZcVgwbjUVShTtoHswJL1j2Ret2Whd0FNSNpWZxfiTi91XKaauU9wwY9Z4lrJpxaJnvrDr3+5b/4lOV46OUw0fypgHUPyT/qJHhapYCoD1BotqmnndSSvO7GgQRLC6pJnYOVQ1IUqpLtUdULcBDvLK78YC+oY3dE+SZCL9pz9dmkKNUq1oKnI2tlWPfxP/67PIvydMpYf56zUsBCWy8p+gyzYQh3n8Qr/4xXLPJ/QOdEiSJuAuKgDGbkUyj0l1ljwngxbbP4xBSHdri+HHpylyUSlWyHqIgg86jmMh6J6UHEe/KFQFCMecQejRIsRJH+zKLfJPjTEHl/zXJwQGhyctscQS9kMidXW2lu3TZpcGQY+++tWvNpzOE8MPAQ98wkVNhVRXUB/qytNqXSiM2ZP7yZTNIGQKjqkwWmt2dt4MSFs7qBywzpuFve4eH2Lbd0vYkOhXS9m9VMtXT9imV+qWS0JIm1uGsuBJHp0RQWj4YJY90Ua9bRFAwlqgR/QLBS90l2WuK3sSFgVMvWZnB9QBvBGEVFuu2jrfQv5RbPrd4qOvLy5crbjXLoZXFZudPXuHZ55fwo1Et9Km0V5nuJDEERstFCRnHG9LrLxrkX9c0hzuGL1fJ/L0Unq9zyQmEEpxgA1soREf90rmJA3N5eSFd1Aycrh9qyFXl23dV5aPY48tndEcQAKG6riaxFpklCLxonb6wJPA7qMXNCTsjnUVqP3VuKRFfjXojgCdFkQTwiK60F+PEwT4Rx1Vwg58Zl3uM82PHw6gItvotfJLd2LYKJ0P3a87RC/bLgvv8XYUZSHEMvf71TRTPpbDMKxEI41IxnrEOKRp1QQijVss1kNJtTEewKJKboyvcwrLf4lAnxAY2oqiT03Km4YyAp7tGCpoDmGYZjfTGYVKMVM/lZhD+ebMy+Z0M6M5kfXIBO2A+vSISbHZOFgmniBNrHwcznPe4UmmRX95IhQUE7rEMrTKRjKayeRuuY2n0oMOKm34Q1lYLyyt59viVqAAQi0NRIWpHNjSnbrDinpvGgnnRWNZEnDH+MCW1cdX/aJiDnorNxQb3VhssHjx9HQBS0WnTq7JBEGJPP3Vg0RH+yjam3p7yJOEDGMhQYDqqqfrRbOhX1wzglc038fI0RaaGFlE0GH+3veWQTl2cJaytyIrDrXvfrckajZXbFz+Fipcub0VHiXtQgtYX3ijUATWl+99rz4b217jJcEwGK7EKhlLALSNmaGFnDUVoDEQIuhRMewQPbJZKComK5j4cQXnc0yqnurJMJCnwHw/ea7z2FzKAxLm5JeuURhwMDC/U9YsVmGGJcNMzpW4hFPqIx0UieVmStlzz5JdaV3YnuMuayOi+dXteZAI9AGB5El9AC1v6TsCJkquMRzFSnKP8mY9U6HpDBMyG9JPJjiGIiHMnmglpiPNg2bk0GG+mk85ofx1UmJPk3SYY1Okv8gQfS9bGZrfHThJW5jNXTIR07suseTTBBSGM55ifSgtGtHTee2k3Pd2tuNONfHBSJiUeLjUUCvq3BYtyrGGfUrx70WLabcXa9jmumlK+SMf1B6UaB268K1v7ZIkRQ5ukZ5VCUmN3mmac09O2ofSPs9dpay4FyVqqEQPGhK6tSeiRVw5yHRT0QoMY8qUMnCem4mKRQ4MP0MFw8bOweJNwMLdHPRHoCoCSUCxQBnsvBLndSgkkXhcDRUz/Hoi+AG3l+eEMOOJz9Nr1nYJT+aGrkQrIOZNi+ggKD772bKZIRbhYw9+OBalNoqRRuBcCUzk4JcCNKLmfncy9zN0njgvWghj9jtqLRZbYEXg5Q33A1cN9QR+/PwVChC/dD1udJki/B65nj0yueR37a/HJEvYkDzsFk30A9ePEWNuVmGUYvoychRhqSNU1SolEegnAsmT+glg3t5rBEyOvBjcXqZCWt+TrgBMsz8bhjAIuoraoDxMoDFvmk89/jIAmNlZoXw844bjwIRoshYFQuXQ2SZN0TwmVgnk4EwoOXO6lKwy7jKn04iKs0WQaZ2O8dcZ4tHWU6n1ycodCoIgoiPgEl2BHMDBmZ6IKKPJxRO2+PlY8aNVioduLjY6qdjjxqLZPg2d2YGLftJq2x5yLbUQ6HGb6hr8A+Y+NFxFaFrc2MNLMiRVhg6U6C/tqO0MCf4KW4nzrfOka2ncrsSQw84tepebocj0oiEGDC1L+6IC6LurrUmSTqHjEQs+Suof8476NxaKrCA3etAb0+jy6Ed/IYlzuORX8MlP9iiMhl3KFkdBkqIgg8RzBR5TK8gNyxAXFV+b5lQkKdK4CoEPfGDO76j2Rj80mPutBcNwABa/SidD5Ia7A81PzDoc520koWt89TsKHs+o42foYUaTK4EPQyCnG9DgjKT6PWJaSLCUhp8JQXF6zUn5oEcGv76GsIL8ulXJLCFAnqsO4H7FANdwpTswe+BVuk8+RqlfDfbWVY9UtcqDRKBbBJIndQtRJmg/AqZaBMXH/P7zn8+ZlE2gYhFMcIgLzS0NMTk6ZoSnTsySuI4J1OwZs7YDCh4Nos+kdIup03xN55lqpaEPnHTgrgiIltglJ6WhPKRXIoON+dS87KtasXjViZnX8yuF5Bm3Nt6F72PgRCUpEnrCwzHtBZ/gc5rjkvOhkxorsGjxNJK0QXHLlOKOScVzrymmLlE8/p3iI/cVq9cmBmzA6CQtRYeJGKQRW4vKROQKJGk1YLrX37aIrFRJnxINjL8K0lJdoxcAImKX6w2jbS3yqVrXmJLvleuHLkfBNTlarcTK0YMKGFEtRA7hU0MFjExGTVWy4RA20LRcViUCZJHgOArHmbuMPX81EHXAlmw30PTe2mpgFUhGnRjzPrUCMU8UBKOtVt3XJjCc8C0GszpBH1WV41vFiF8QFgIZ+QPEL84v0TiEj2GAtWi+nxJHGGJqRPkIEPQjMiT8XvSXX5ZjLcUm/V64/zRBhuy7GqueOJ9MnHEgZdyCQUqAPhpjclC0cn242Cw2lAwIJgpMqDJ0SYMb8UJGJsZSSiLQFgSSJ7UFxsykLwh4iPR47WnVbGvGNFHShVgItWGKDDGNmhBNxOZl6tN8at4kpkLp0QUHplHHDnzMpI5D2TiQeVzyl8jB7Y5N8WZ/2Zrc6UizvEycpFqE3NbxJKG44jNQNKoiMizz6hQmqFgWNPdEO/+b9wW42FRaPakNKooSAggFEEyRlSXaNX+pHROKGesXt+5RnMYE8HwxSQj4RsU/ti2uw5Mgo5lUoDw1GYAoJvwJkiQcysnWoneAQBvJQaiKHNQKjG0RHVRp1toMo5mqzdzlGLdm7MFyWoj+rTOx1CW2GyGWgCdVAhxFBD+oTjY9MMxEGfNzBaWTBhQInAAgK8VaVEyVOIMgxuyBcVb2KoPQwEMsDMXWAv8eiqGiVl7MZ5w0CgLUlD+p4Re+MIfJgdoGsDLhwZQYMiqsAnKWzNOFr5yJeA/bmF+fgRHebZ3IlIXXoo8+ymJ7Q2tQJZfc7peOaDoGoI+hLkO/r6gqUhW7qTmjOIPNgFcZw8wv1NOUeUP0kl6QgyHBq8i+pft8nElJBNqLQHeTYntLy9wSgRoEMAw2nrDlmOBoaHMcvevYtGt+NDMyueNGFDl9Zgr2+EjMj6ZaCRz46xNfTcduITFjOvDVJV/jGEmKbHnrWPKFNDlj+saBnA9jRpj9yxs6xSvfDz20XCjnFrWK/OdeLDkcX9XAiXgOVgoaCFaUhBrSH7SIFjlJuzTK6GLmPsWpHy5+tGpxX1ydWYybULywavEwANkzROTwWXjyBrUWoTtE+DbfUE8ogrv0BQVGqdOLPkGSAuTG+vThjHZhEiGKC1E3WjbGgP5i3mst6gMoo6srMVp0et+E6ZFlsSJJkQkc8AmVFFVt1HUlKuZGuxAxKWFaxqGvBtgFF5RhRnqkxb3yxP8w3R6KH4gh2pQ6sJzhQ43iZ8gg9PnPlzuTxZ4FKJ3fHaaunjrdgIkfo5ROcp+psJ+SH47xCQTgcA4iT76GL9KNOJNLBi126EbDJn5QclNJvawIP3NX9b4PQPBOHS0lxGTlg02aDWynpG7Gg/51hunLLhVwS0kEBgiBuZPQAGWf2SYCXSPgGdGEaB6s1DO9ZeLzZMmqZOb1tIon+esh2wzrY8Y0yTogwXgcmGRDswaPkcZsa2qWM8Fs4kbng+XIwUH1kG2W95AqffAPOsCMXIlIWNlSTl1NxDTHwAlkBNt6pqcMPJGLcIcG3FSG4mFjs2ioakhUAzyrFw8sWzz+VLEky5qTlgniVNPHTfY+MrzQRw4gxUU4UJzcb7+SOdF5PRHYugV9BC+gWiv1nmRYl0a29LrOiv7Sd9GDOggaThJ/e0JxxN/ILWJ76dc6EVUDhz6I+tDiaFZTcZU3GZgWoFUDu0rpKvVvVwK9QDRKD8bKL1/ZSNziDSQ2UDXkmop7ERHD0r3diuJqI5mq9ABkpNGVLcTPCofDRw0zPwp8SJNxGs8VRL/LXO8YQn5Q2CG7EdLDnWdwyjl+pGIHsTppsCKCHeJDMjFy3F6J3PAqt4NFzzqWQ/Qyy5NJwO1hS9ZqvwVVir7zw0eFmaYwqrq9RarM8yAR6CcCyZP6CWDe3ncErFuxVsWjJyOBedMMGAYhf0M7mh9d8pX1wmOxydQUbFLGpZykAs2kZls3ShDHauNM5BM1q6ZjB9KYWGMGd0zkhi44Y2qWQJU8Z9dKuJYice35BXYMGd43H9pF2AeqFI1FdJiatFT8b7QoqjSzmHhtscnmxSWLFc/yYsFjYjHtnmK9ZzbZGG8Ao30C6Z5QVEI9rBJqHbtd11KI0VJ4APxZCCptXeFcl74PXzVH9UKi1xwrFyXV9UqkGtnDeiJYJn+iJjMKhoKPu+Rga2m6vw+CmHpBYWuxxZSBxBwYnqZITNkju1ZZBknqKge7eHOWffSjza97irD7NjolpAkgiEi3AjowRgcZybyrPMt8l92KlhpsfjLu8sHR9Qv+5IdpCBkAOI0DvBb78dd5P1JjI4K4DU5n2Jnk4EaJZYjuqIkqyTDIrgNj27EcdK5sZWIMKMhI89WglV4CNzqJEtV2nMwBq54picAAIZA8aYCAzWy7R8AkaH8a1MczqEdJs6QZ1sTqSTEiPEzBJlZ/6YNQnDGfmkPNp+bNap41jdZKqITaM3HsfNziq2MTtBIpLdn6yr+AiCjLmcoS4AyfV1cZysEqmwUjnrARHczSQ7lyaTsxOjAU1+LVHLVy5qhXrTTq8e1m/33p4mGWpFuLzc6ZsMe6u6xrKR+nG88dkwbYLUTna5NPrwSA4KIXQcTeIMisK3B6lW1tYn0ddFbOkTku6wxdSMuyT9gGU2B7D0VuCKX28iKFswwL8QqziBPqYSa1yRg7u/X6qSp+Zq2+V8sxv6m8kGSPBGLdLEBrLYZ3ayLFcGI/VQFDWJd3xGIVLcRQid+OZMC0hN7e9IK1qxHe4l42LT8QvwiJ5UOCwqoecybxM/QxMiXzU2U3QmuIQaLJxknUzdf43fkbHSofv9ngQ844jwD562RVW9kSHYdvGQC++nDk4bv89UibxO4ydfiROp+SCAwQAsmTBgjYzLZHCHjmZg6heJAhEysHBF3CZmD6I5SxOdqUig0EMTJdmjTNyKZIHzNsTLsSVwedBdfY9MvvnXN85wW5mfGrxL7KuUzRaWqizOxMw9hguXiIRchWDIXeqr0xrqJ0FGEfRLnmej4R87uwjNANrfLR5hkzFl9iide8ZmJdRJS9ebAH4d7RKLDsvNdSLy75rt+fu+XiD/77hWKRqZPWWmXH1V//xjHUniYIgrGxJxWlmd2X21An3cTDouE6QiYyJBWeDcn7ckITaMfQrHL2VT39VSiqZy915KC3NbcSjfHMlhMUOWsKutnbHKqWoAhRt+pM0wM1t0sQ84/F6gxvXjQgIKmNwk3s40UiPi1ENQw2PyjtdYBicuP2sO0eV2zhwStt9Pnp6W6kBGfye5SDH4XMDWO/IL9TJEkyaUhsZha1MkL0nZQOog7+ukVlfCrR49K4JJkDCdAs1Y4BJpnZwMcYZj1C0PWjqx6u8GbbKcE5JREYIASSJw0QsJltTxEQJ+FD8XgEZ5yIWdXN5lCTsrnSBO0BGqVgajKNhlSzp69mVbOzv3Olg0U/jinZzgPX5lGlKqUDhUaJ1L/ZXz6okuAPJq4pU8oF0vZd5DjgRjE1I3CeaH01pxPv4uX9abG4KerQ+FdDfvCD0ptAu/DL0Fv274lFZ42JSxJEwYpJoZektjCanq+AKMqVPkceWRpIrLjWCkYmNGKRRRa9YudNL79800VmFFu+tLQbMaJUoGlIn4VykpU6/+EPZQepiJ6qIO1ztrU36gXZhsZ1npYlRojuYKThG60aUntXt8dsHk3ftdztjXUJjEbUx3qubgUsNjFipxTfrXcGQuz0zQ9lxLYQ0EUfQVXlA88W6atLRvvuu5ff1B8NMmx0gQVufpWO5Yn/YY1+m2ycvuoXw0P+CJNCHcQZBNfgdcb4ZAfybOD3FRJdCSgfCZAwf+OxBFVSYR93EYX66SlOoc7I0wiRRik9Wac5p7z8lwj0HoHkSb3HLO8YAARMdmiKhTYkplScCYkx4Zp5zY9mSWLSrKROPbvkTIhgBo+vs+dwo4pAzaNKVSYOYo52uwM50P1sM8I+hEaxXlDMBx5YLmozO5vTaQ4kyexMPGpTvb0VRchTHLTiQize0UDvc2iyjunZZztO/H1x8p9GKXXs2FmXXPrkedf/Y4sPPbrGViKWMCTP08QqNjFG7CVgDI+YkwKYmF4UJ1v1dKlOXOKAi60ZItyH9vKYjgbhE7qjK+Hm8G5UdbaPEdx0Dd3WRlExAu0KIpX3lY7XqNCs/S8uWHgfjGr8lRyg3u7SEzFsmEvhHI3qyS0a2xOnWGSlmyxbw5y9lA1Z6UqUDjcsJ9bPd5Ws8TyTVbx7mBUnSIzFZey+KunnyffHrOgXatg4g+tLg1HFg4QSPeQoOn4vBok0SJIhGiKBM9Xgkcxv34hyXtf76m9AYYwxXPn14UaOTRQGg6vuZYoW2c2TKKuURGAgEEieNBCoZp59QYDK95Y3C68EkbC4UDCEmd3MyJZjLjZjmnNNo87UaR0zZu0ZwQ+dBqQgRs3pUW0VzctmdnO9TGRF9+AWnlxtseNRFf+IPaDdImU/hT7zCvRaoVeEm3AcWE+udSGqweB0629vWPOkszqe7FhypSWWXm70fQ9Mf/7mm++77oKTVlhv/HKTGXVUjAEJMuCifjxkV0JFMXu0ELvOIDqwVRacCZVD8TAVxG4ItC9K1CgqyagjvkcosdsVSlmGLmxM3NszoTt1MQm01Ydy1TVBCnubYWN6ux3yroJXnlCCof2xet6zbrEkDTLvf/88Hd9YSpzRBUDuNp6p9nauMb7RHopqK2K//cotqo1hA8mrf3VonUjGx/3Nbzbv0LrEdV/h7xOioxk0GTf9QpmRDBgYeqLAoohe8yMyEhSnDoCKsWHAOKN6kiFzzKhRQyeNt9raShOJ4yS7kQTV0JK/j7tQImPeAfE7FUGoGmF2Kk+lJAJtRSB5UlvhzMz6h8CUKeVaHpOg9URmWKrIgmRTswghD5GmSA+U+IQny9q5tVmZ8WhJXfh0/5hpkq3ENE0fh/uAyYR3yYuizMgxKVfJ+nZg0j/mmCa3Ujn2LfSsTwmFMGh5T/tqZ9/+ksces83kI3eNmfRo8dwzExfrmPmSGXeNf/yRS2+fLIKbjqTD+DKAgycJWBZ7FKSnSTE1p2yHiCuoTwi1VwlHX4Q803NwQInsE61ikKmETkJqWZ6EEkvmUqXMqjTdHoC0sR+1RaGUnxbJ2YdnkxlJDVkR+q8LjaWPfKQcUVXRVpXT3yKsey4wF7jGJyWmmDED8eKZsjSyyjOyMlx1ClrQQ3A0lgmT/dKw75UoiPOaMKtY3KcHa8VPho9YhJZq13ZibZoeHitIcLoWWTpg0Govg5xxG05kFiYjKgaVzmUc0pU+DhAgIj0o3BhSDb/4Gr+y2pNqG8TLgVawV7kd5p4KEHQThdHijPNpTJqDaf4bAASSJw0AqJllXxEwG6JKtC+jOleRGdakGdOxLE2jOBPCJBkl2lq8EcHGv/biaXi0bnWfIugqJXpQpqoVRz1bUm7bvaaWlVZ5NbsWL5VrdqV8JrYUXCSSGNg//rEMk6J3p4wbi6HNeLGYPqt45tli7OiOcaM6Hnth9L1Pjp41tgRBVTEqz9bYDFYnUEm1xVRhmTSH5nQlhx02jyQ1TaPtPkQRwpu8o8Pu1QqqhHZkyhJ3r8TgB0p0EMeSKb06ru6qOejovDpfFaPH/dULYT2KA3VAHVRDu/ojhhNUMZtaUQ27LejxsJogzZJVNdeKpuTMSZrbhzOLYUm2NLfdJYQK2V3J7ZS3joCYzZBaiyLQKWNeJwpJ1swWHdc6KxzLO56F01X1ByZeaPeHtoiK6QhbKviRsl35MSLlvG/84+EuV4o0CsWonEFigBkkqdsKRJ39rQYS36jbQ5AwrMhqTcUZ6kadeUBn6QKIKTElERggBHJwDRCwmW3fETDx0ZExP5ooqR8zL7VkKjTzmkbNjz0Re1Z3kiRh3cxK8+nj6vZKIdHHEWPhjMdxf0ND03b8gIr++tfLqb+f4lm/mvcbs2Kb8fIvmw1GGnWYvOhGG05bZamOh14cteio2aPGzJ7hXSz/Kl5284srz+6kQfCRLDwaQknU3PaGQo64C5nBeJSa1jkIVmMFWpyxmFyQlkCoWrE4X7BOrNNWB9VWmUoqVV2diYNxxbRVisfWLO701plbi/XvL5buGDMxTHr+uksv05R8bboggqtwCPYbQVS1+ddlW/eViYg2rQTNhRXWi4M2CpLEx/rBD5aKH5e1gkxwdIhC7ZTNoNVCgt9PmVIqbNsjycSgRX2Az0xlMGtRU9Ec9zL2iPfScNbBppys6b1dnWQp1Nhzzy3HMxIjugjR75vo04gRVDck0ieIsjqjyFzkxqpBy77rjJor0QGRHgh600kGJ7GGTcWzUEWyI4EbSfUb8VUOvhoJcvYI4YxSgodJ6aTjgdicIuqTfxMBCCRPymEw5BAQoWyvZJ4RxoNYdezABE3zVQSiZaU7eVExWixT59Gozrm3RoHPf3NMzVSCA/njELRXEDV6DvkwFwvFZSRgOeinRNBVV5lQLbVLx9Xn4uc2XKvYY69Rf1h69oMes18sJlxTvOIvo3aeafPIuaREMpW3/Ipqr9gGPe0tvxZDMWngHHXiDVnR6rrzrb8efXS5t3Wt8PXQnUxfAZo8faDnE8e1ieN4fPHcnsXZexf/t3Jxr63D2YlOKPY7edZus4rSUuQuNgMx6YiRYUATU/k6hQ3D+Z4L3iMOjCszBowbOTGF5+tW0WBdCRjRiyOOmEeSpGSu47Hy4rYWge11GRo2ERcPFgQL72/sdxDR/bqGV9FLY5hJnGmLGK64cv8FrbSvwbHHlv0LRoSYGYljNzoCC+SAE9BtOyhMhfirCUqPn5IzehOn6Vu7ION2AkN5+isrw5vZlSgdjdabZgZMDl2z3lOylERgIBBInjQQqGae/UJAGIqIZsoGNSHUiedsPIndAlUKMfnGNDrne/0/3Gj0zNLpFsqnexUUuZlqw6RhXjbvm4g976IC7AF1MR/1Bfbs+xprdJlOcZwUdcJ29pviLXd0TNm2uGaxYtptxeoXF1vf27F6lawCwUEoY0D5UCT0Bx8QC0ejLYTW74PgGXXCHUYpopI8dBitTgllCT3nm0nHDsV17yqOX6F4sPO1KsXSxaPvL372SLHMucWrIr2af+5zxU479d2yogdxREaaWmltU4EeW8VvflO65BzUiQ2EOOaEV/dWoGGjJgHvLFu6o5IAiqUE/7N0jsqvJRPqDz1nJLCngGGPIvg5YCpTplR5DOwBym4ZJqLpCUFNfFCi//mfkpdgdSGGlpN+pDhNUBljwC8FqQoKaBiwGOFbXYmfczWA69JUBNd5aeJXqSAZqht8ZA43Z/xCnUlJBAYOgeRJA4dt5tx3BCy594RNSdisxQsWzIY0DWUcPIbaIOburibZzoJRpR49YFb5hD5QljnaJ6Zgs7zlPPgNrjZrZsczT84aP2HUIouNkbgPss8+5cu/zOyNwlxRq0qrBJZkXVxse3GxjQLn0r45F5s2H9mi6f1Fj9hOuEUaeVLTG6sSuzqgmdxY23D56xSbOlFj6i+814FktUquNjcOqM2Km5Yt/oMkzSjKXXH+Wyy7XPHYK4vL/168Un95Qeqo+x6987wZD09dbs93LtYrG1JVkGrwnfVK6N06XlV7OzyRhj7wJJkwudkGQhewyiglFrTrHa4iDikWyiruHnTsNyKcaH0jxFDHNtgXhZkDmf9X2LismNlqu6C2nu06VhMr2kSkIUlEp8dHna3d22OPOZYbNRTLH75CCdQ8fjiONdBVvyA/VR980aholK7GSW1KjZWPKhGAIOVixkFkQoCJnGUCE8WlJAIDhEDypAECdvhlayby0GwJladYAcWMKIMr5l8fATEMOeJSI9aEgce0q6oxcZtDHfRBqhsdmGGrSTymXbN8iAnaJWzgFTvMfuG+Ry/+7RWTH/337PETnlh5gy0+sOXSK3lXZ/m4bJ2/p2opVc+CI/YDT/9NxVVUqW5XQHUw4xPFda05wnvYNNf6kyDig5ObpjEANArd/LWvzWt1Y4KmZxqNGR7u2a50B5LkqlgiHIWRoKtwHBEskwokcVTnxg1lIXgX/+EyxZN68iXFfbsX52/y9PUTv/X8Q2NXO+3mHbf+8LZrrdNrQmqoCDbqVsBO6QoK7nYISdlVh3ZbihGFTKCSSAaguBGNaoSJKQ4L9CurFPyXvlTGTnk2qKuP0gmqFKFOjG0yGQhRPescbWGgLN40mz7UiloZ4aodHCgu8Y/7hVruh+IL5TZ1yMQYVmfJfHXgr3EiMqxubLtU19KquLjkb0j184QVo6kijHA18XMLrmlPhArGKpM8SATahUDypHYhObzz8fhovTo3v2na9GSu9KR7xBELqFEmUFzEX4b0uvmO6kUsaD4qhK7yVUozsllY+tbzbFe118BgJG6Pp2HRPI5RCg+s4SZQEN1vgg4FudgLj91/+DFL/vPS6R3WgI0aNWbZs657+1uOf8eYCeM8dv/0p3MiVeVsFZgoYBEtFGSjaJ33nNDNPF+hNsz1HBlCPah2vVCFDzfe28MzGjKqo2PvZ056463nLb7I9LV/vlLxxY/X2ZRobn6oXkWxaBqDir+1IpDIEi1bTlOEoNMWeMKNFSRaV5vY8fPFpAeLVVQQ3fVCFWe8HVUgym3FmssU/31P8cddi9NF32NR68+87pGj/vHXp8Z+9Jdb1WXSrq+AEtQi6rmpea+2FL4za/36LAYbe1KtVc9IY1iqFTsv+KhSo1QnsU92MiBbvDZlSmPCfp0BupUK9l5q8TZDBag53lMJnnfIIeVJgWt6XNcjf36eiKBqx480fmv++kUwzcZVA0lD0B1SNbDKNs7EXymrXyszkq/xk1QNgLAksbH50UmTkggMEALN5vIBKiqzHaoI0M2iNb2lvJqeLMui6thFFsBLXr3PgcLm2jDTicCwLbXtE82GIU4KdBUJIZlZ2MdcKUpXAp/GGbYnGLur4lie8uVTKXUzO26kUHpIoIyUrt5/3+yJl12wwv1nP1dM8sIGE/vkWY8tf+rxF/5uu+32ealdiKooXelto8cwwDfRlc+IaQHaPBr2iJLGe0mtSrMuiWrhuVA6k0x/pGP2zK89e+TbOn60TPHw6Bc7Rv9sXHHndeW2VLbLnCs4Dbpmayir2Xsolk298531afFaS+KpWGE9LApa4QUsGIA4FQ7TRvG694uKLbYqNt2yuGxaMUECW1TeUmz8t2LnDYu7X1mcqyueKRZnj5heLPKSjruePefUi8/f/BWv6t00ZcDYx4HBrFthDNPXrYU+RqciLrt1yv5cZWLsyWBmRBFNr0r60/hsoxgMnpRajz1ExzK32icZvw5rHu0x4cnKvSLWPdL4OUDVqCD+GtI+RANd8hOLYwctmizn6mrYLLEi+fiZGL34mWEmc8cIaC11K3MfGPnmN2edfrpdCoyY2cA/7f+zdydwlhVVnvjjZWbtVZQUslpqAgKiqAgKioolKtrggq2ONrY9OA4u3dqOdvc4traio+P2/7igf9u/SyuCNtruOy7I310RsRVlUTah2KpYqoraK/PN9+bJjLp573sv38ulqCXP530y77svbiwnTpzzixMn4n6lBBhnpsTZXHcSDvSmgHaSSs9WY3o5AKNkkJRzptG8DyEcAyJJRVcwhNzstiCVFWVO381FKD4aMBMPipBt7py4ydzaXuTFCF6xnsm8k99C0Uyva5NX6hKyoSLNR+WZ9Wl+pH4n/xSVDyXuZr5wbbnBg2piFQwgo52RVb8lC5sP+NMP56StG0b2ZLl5Z9r7Ps0bv/q+X/7kpkMySCpSj5CwX9aiHU6ShCGHMIA/ZWlFVAkQ0fzKUd2jOfby742Nj/5N8+1L0mj0bJM5+s53/v+fLz/78Sf/rzf0674gbQRJgQkmjf2DPsOAtSzK6obXdKhzhdScr8U5OtyQwKsoLkaLJbOfzitQMvosP3VVOuT/Sa96drrfivR9y3DfTKd+Np3+p3T/R6QrlqS71hcgqUAuW9PA3WnxkpVXf/XTdzzuCb2tM4Fr+bjOctH1a6cncE7Uw8KsJWkRzyIxIPzhwKg/XrlDeIioxWuPQA/OZYD7g/CWVDv1m4zpffHR8HGZ8gaF8s2W1+DCRz9aAPFpxEk4YGm7M0hSGSGDL395tVLkBzwSO8UXpetJPj7E2CTeeXy5Jg8qn8emr27mr5V8ISE8CSzlr5SkVG6e0nA9orYSQEgczCo2Id6t5N/r17Pfu/WsNza2bBMGRX8VKuw5z9ry+S+NvDao17xm0+9qHJjFSbtaj81AfUP71DOm8gARpwfx94TjAUyxQkTLB4bIj4Syo8vq5CewhtdB/HImSs1xOPJhpzlRaMBQc5Qg5OTN6oODBTay0BMedfaGgjbn5oYxpZaeIlZtmZdVbVSgneb1K/XqQX9bLm/Fg/6KpaCCKV9aGNyBJ/r/zgE425tHw2vL0IZNLGJLgq4qCyuVZBAGG1wmWIQdtd7HQTUV2re5KoOknM+Ra7+8/GsnvOjqFwuKzxjUWhLvF98hkKRn7b0SdGwRChIFnYHXIDdtBGt3JkI0pHyuEtng7QAINIfhLJPWPfWp/Vdffdjbf/0/35H+Ue+NcTWMJoi0/WT0OWmbEJdNw6WjLct5tb/W0TCKOjvKqAOFtAwOFoa2TCrpZEjvEu6JiI3gHn6siH32LATp2E/rknCk0KIMQxl4p2RZrjKswhHSTmLbVYB8xopVuwTd3FcopkO3TpTgDbJzbULyohuy4ak6KLHQbDskpy9ZQhKQZ5lLHBQXCg22x80ODZdPxlgaa8iTtJgg+UoJeFYCladDrH3jcznn0VKn6d8nPnH3q15DMvPbHIvh/4Uv973whevOPXfJNBUym83Oy4FZnLTz9s0Oq1k7/UKz218tkMXcmuKDG5h2ZtUsHO5BVBU8YTIKFtCDfmVuqX4qMqbg9JpnvaxKWGiZJAabGGP3lS7z0Jj++nCr0LB8EoJ47EKy943pFQENOXHVeBB0E8qqUDlImY1xZFIuqHztVxqWbhUhMSGZ+AryffrTi8XHox7k9J1HpSsuFVDhuAA6cnHR7qWHPe/hpzw7CXCpEA6wvpMgzeTDs6VIMydLwn3GTFMpiyVp7UHpdj4eL2SFaaJ3TNlBzzPOKMwb9xKXgDXBEAa84iezKAk8SdYreQQ44DLRfbFdi5FzJJLXogHB3jLG77JlS+E3GqO+y9Pgtenwg9NVQ2mAMfUeFO+Y/83ejz/+pGycxtJ299+Ssbf5er1GO9LGEOMQP5YY/3FAeJlO75XsK4zl1Pwg5GHsCNgS9pfhQvyqaGLshWuYj+GGidK7J49EN3X/CNgBARvRauKjOHfE1cGy3UMukUDaqPsMRk67ch00wTDkWFIKiIwMzAx0tDdT+TrfrF8YgNHMSE9iDSs191V4n69UjTmP/qIEeGGd7eTOjJE5TakNo8XwLY2f68xY8dOVMe7pphAA/Cz34HQVsVvm08vo3C0ZMNuoVBhCgb3QSYXsvzUvD6LyOH58jC4Rx2bqrIuZsdUEARPllQuGwQwPNqLLzFOZigpIyqU45phqM3Qp2dCG8VdQMBikRN4Ojzsp0Xvv1RD8YlrM/m3KM9Qt8UgJh1GUKIY9pR+ZmPiyCjGJD4XrEQUBc2EtcjXaXVhz1DTvZxge7n/82558y8svXnbb7wQv9afmcGPujcef+ux/PqJ/YeFgs507CpUVtjhbmcWdHHFfveUtRej6ZMmpUf00ejHhLdGqtFystBuComwO4iIK0kdeKOZTIRxjCH2mQitWJB8SAocBnSyZv0holC4Awd3XI0hxV6ZDvtR87vOK8ydvsMtwU1r0vXTqDSec9tpnFQl6JSBPyPNnP9vVcwSDN4KYQXJi6olZmdgVlcQoPduByIAXhlSIcPq0JGBdxDRxZeyhFsisy9rKzRo0p9eERCYtHZpOKAur5a8ynMRGlvGlsfapZbmdMLdIAGdzhsG7fEsZVfvJkMRzMxmqAMoPkAQc462/SiwX5DosdFyUf8rVkABbPIv/EoBNstILKFa6ccACnI/WweKKmGGqDKkoreXNGa7IZLPHQyodrzDQpJcujV0sk81vD3puFiftQZ3drqmMhCUAZwHkJYNIGSCj8pSbXukFwRhpTJHJZUVDWdKCk2xsOeGEQjVTrB2IKmQqaMAyUY60ORBmYmp6CpaxQHwDsBfLLeoZbOJSgoTCnIsaZsN89ZcKiPkxpVDWv36SWFXBKWt53PUTkscpfXXz7FFPXb7xnf+w/oLvpz9c27947oYHP/TRr3jyvL3my9Ok3EYhe75EVklpAw6HRCj0CYuoJ8AQcUsWfRwMPTn6z/TglWlwebouPy6646fp5E+lU91hlbF3R5IFJp8KYZGXjlkuwTeeGL02MDD/j/s++ctz73/fW387sPbuG5YcMv+kR774FUsrq5OVfNp9JXXveMc4AYiUMBABIFoVYmvPOKMQjDIRcn4vosh5COEJOQKaW3IPHCFRZLUn4tEBAvQIKRIKjRXdhNUffHCxrOlvZ8JSS9g+7CI8ZIZTJpONvNqo9PJIKSdreW0QWb0VrFY+PUTb5YM5Jl14pWkURbh8MEf+fkUuoqx8p16EQWTQSQCeeiq+xh2Jle6mCRIOGCwYaLzPpCcpKmjq4VNByuFh2jWgEr7FxI+2REaBmSe8S6RxeJY6c2AWJ3Xmzx7xq3HCkS4shj4NYvvDSdOy/eALRws7B8EYfnUyUTZ/HRws5q8twVZ+hPqj9UJ15ps0eFgyzhUa0MSRFhYe+9CHFlHkfPuGN13MhindwhBFTC9Tl75y5ESJ5TzpU199lCUrCK9eaC69fBGQy535C/sfdtqh32/e+8p7rX30U5aedPJeCxaOwg1huWKnBCqZRocRhQ4t6Ew6zFaL2HjH5HBRTILOSU9dlNb+fXr7YLqKAhR6dEF61qfS6cOpQHUchBi4MxDIAk+D5hZYSRS4sN9+c+fNO+qmm46CSw5fXBzAWI8c77LmzGcdtWi+oDFQuyVOsn7K8Ou1MBt606Jh+bxKXjdxS6xLnWARRqgnUhnGPvsdwWuo0UIzh1+HIWMyAM/Zf9fZnwS0feELSYv4DwyK8ljoqZLtEkMwDt60Ks0nEWSuxfQaicYsqKR6xo7WGb86NyoQw7BSn/gJN1Bc+xtf3aEfjCl3XKDIwa8kxHzMzcBJuIdpk56cFFlPQIGTKolagqdKmp3lK30uYk9km+GPpaaRnP08gpRhxYG6s9R4Z6rHLE7amXrjnqsLK+Utpz5BNGwHorbAIKtLkEo7ivUU2bI6NGMHol4ryEb+8QhTR+2aAFGIPPngFytlkAvr9spSI18y4cacASb97vsqdiHm6AyexyPeMzKkXl2wNGGVJfMVqQBoUp/Kq3lYTaVf+MXbN33nBxsuvHL50PCadYf+dP2Jxz/joMVLiomYikGN3D+Rm9fScWaoEqwzOdIQugxEiAp3l4l3/Q7H+ePb0sIPpL9amZYPppVzG8Nrmwu+m465Kh0uHxFIDgeYnIemQzWA1O9+bcO1l60Z6E9HHrP48acs0YRuSANZOJ8dQzqIcSUqdSKuQD9JE7yFRdYlTRUqC2EWCh1xpFsrbie5OWW04outF1G/wwFJpCEMkubDXSrEXunkVk0MMU7TCmbiSQWSOhs2tvC97y0ixzPKrxc99TscVOXVdp3ICSqUmzBgZoyFGGKgkpq4Qyp8JAhybWT5BBEGFA/mO/Grmz5YASdhlJmJNqqAkesOuIn/BmB+oUo8Po1/Tzih77TTtn75y7LMkj18yCGbX/ziya6vT2PlusuKsBF+koNjCITVKfC0OUNnceou+9081SxO2s07eHLNY+Y7E0eRrT31KXt+ijqLpQpW2RJAO6JJOxAtaTB7HAACRxgqhiT2kYE7DAwyeeVkMrs1mzT4wR0KlyY1T6LHzTuhogBtVK2fQBD36WjYyOKgJshHsIWz+6yzoL602SFN69OCV71qnvV79Kc/bP7zP3340Ou+t3dS3cbQdXP+/IPf/2m/v3/ISftrpik7g6qqtDZSiuLER1tJmZwCgu3sk/riF8fZjCLr1tR8RLrkienH4tpvSAdemB73u/RA+7K/2HiSujn1h8XFwAceUixc2pQEAYRBap1Z73dvu635kXeu/s7nN916wyYh7oOHrr/ud2ue9t8O3O+AbFF6z3TKT4RJDrRdzgxv2xGRtgLI28ST5P3BwvXqjwuxZ2b45LhSsltOj2djX89cerOF7FOJBLqAuDLt/C4chyHSbhJa3aRcjhl4SCCXPXTyDyJOkO6EJznxJDmUtXuQlPOvV14sPEQi/L9O6gygZHJt6iLmyTwBoDGmRK8baNqiXQGVcEm/YIgB4jqGTECoyEfKIFUKFOUOMfZI9IUBbrlNbnLG1YhQpGGMYnHxAu9Mq2aCjjii8a539b/85eo3VsXUWLp0zvHH7zJLVsFzzAzCWB9c7dD7M8HJXTTPWZy0i3bczFab1hP+2YG4baGWdgR/CObwl+OH4WmHkwzUCUepkWxs+0tF8tOIt4BvqMhMIlKZf/UR3kFdSkYjcMbQraFtQ+FKLx8PSsloUbWQlqxYO1hNVS15fOPLmy/4px/+RbrggHTrTemADb8/Zdv6R6dl889+0a/++rqvOT/aW8nkI49Db7rw86857vCfnjpv4QArYoqslKDQ7JrM2ebAzF4JsHM0Dpd4xZHQLp9T0rdfmT60LN1im9ijUzou/fxj6aU/SMf39/fjALOhPhwV//APRUu1/atfLXaxccvl0wHa5Tzx/WKW3zzn/avP/+Ddc7bcaefP7c1lP/rjvX79trnnfTs989nF0Q+TXnycuPSOKbg3hI1bJqtQO3kjG9GD4DiLa6deS+hDDkEQ+AYW+chHijBYhMOmBKB5y6VqPlrnSshfpLOFD0ANNvKI4kAELgrdoa91jQ6y8Edm/GQAiol28KO/ziUiFTaZ2ioot4DjlXblrxoofHBknjMGr/Jv22386C3VQMETtdJZphkheCpsg4JDs4wm4WWVeD51AEoC3kVe6mwRx0iE5NA3v1m4yoxNgytaamHa6FNKzF6kwWFFw4uGjwuVKcMpT+E2GQ4m45KvHiHScWGHphLVxIM+/MqmBDOEk9T2sMP6x/sRIY4x0FG0eGcnCpmACWnAN6RbdY1Zk5uzNCEHZnHShCzaExO8//2FtZ4cUXArVhQwy8GJJpc5XLSeWyjo+v3yHVqb0ge2IqiI99ixNPQvhRukOCZEXDC9rFAa03HYCo0wFCqVDQOe6FAqlUZmCKWhcCloittPbINnTzutufrT3/3n9K75af2mtOCQ9IeN373k/Je/7szzT5r/q19YrFud9g3L4+Ub90pr5l92aRp6SqMxIP/IkGkJkrlrSnwSJPi9m5NsIue90+r/lj61LN22Ou2nbmbdQns+/+YAAEAASURBVMKekr51Tf8hty84QB2oQoxyRraJvgAaB1ZBdW6agjttQUAMttCbg4OFQeqe7rp10/fecfGWz3zl/um6Zbcf8OChE69P97s17bcyHTSU+u/ekG79UeMXlxTWnYnVETueAF+oAgdE3REhHdRS2GIRVrLcfOJEijoEZctHAjgADsuHDmAmBAMGVYgMnHFG4XxSAV5PGEtB4enRC2wVyh4jacQ5nXVWetGLivu+knnCzNmpUJXUa7meRYpWBE9YXmnZWIio5A4psgLgnPIKlEM8oLMRAd8TCTUEOFQ+PEaAF+eWeUiQavzN3xSVxOQyRQ39ikxU5MbXi5+IeywGoKGBsJ1sSIDwQUuR4RkAyFejUhN0nDwhSMPWT9hl8No8C055CuKUgEuJpXfh8awTiuxmaTwH+OHs6BQhwP+NV5hp1yShrXTi+Idmv41yoBftOMu0PYYDLCsNFUqtm0YbhLQq3cceW0dgj03KmRN4hdbzaaO4u8l7VEXKHBnnnATqBk/YoxerSHQxFZnDXOLgY/EitCcdKo0EVL/AFNXQKDqXdvCRj/sMhsdv/vPWY3/z8QVp/U3pQObkznSv+zRvXvqNzwxvOCHgUfwdq3Hx9lbXcrDEoO2mswGP/FUK6FaebY89NfH/9iBp6Ph08RHpqlvSgT9Pj1hbeLYaD0uX2UW/Ku0TZ1hvSX03pYNWpgPWDPVzrWkXbShoQ7AtV5y1PKaOcXIf9LSuxy9ico/0mqNxrPhwioRhc3NoqHnTVet+cO5NV149sOgBBz71Lxce9ZAGE7X+ri3fe+337nPO2xalu+3Wemz63bHpJ8L6f50eHnvAwxhv3GAvZJ/t7vBBznPi9k9TCiLHOyKWyEd3a/vrX98ia5NsdjfwSvln3WrXYYhQ+X6+1sU2FojtANkRnwd8gyAnEhWWHgSx9mpoZOLIZPUrlIeGC04XAycv6qk5k+ajPj7dkH4n9iOQqFNyNQR9jBQE5GWCbAhzhbQxjjwA40wqbKcgMJ37lMjxJUuPM5HSEMOZWIkjgRAYtrtjvGhgjBrtRSGi7kiGJ6Cb+Qz4hXXuGMXSh9sYbJIhrkpMZUVfVCo/+zU4oBe8d0inWDuGWc02ef6mfRV+d+X2LE7aXXt2qu0Sl8DTkJV45+w44YW4Qh4UnxhS7hkAxSSSOqbCpkgyoVKFbTIARjtTjeLtEMwSU1chKkB4kGmT0+eggbA0odlBN/CFblVV6liG7nCne2vVJ19//ePSDVxXY3Puxtq05LB0xcXfu2vdsY9df8nn7bqL95bMTxs3p7mrDz5u3YY5jZEgCejNwpYSZShbW229BWJaaejU9O03p38xD7QWcVk6+l/Ty36ejuVl8BmZjRel8XhdkP7il+kRGxuL3WR1+EU4JJzdwCzBrLiHk0xpIIMASR7UX5wWThDlYXLsgmQb79r8+fdc8+F3bly9ZZGo+gXpj//27mVHP+0+H/pw/9y712w95zNyuj7dH6/WpL2Wp5tOSd/4ZHqh3nbHSukID/sIgKhkazcQAzOv4xhLFm7qFBZXntwejGgHggghGKcjtiTLuEFZzjFNGBDsaw8jkywwmXumJQHuEmTbPDhYhE47IUKryVilVrI1eTCmJiQvCAKVwFbGTIYjscPFKWLAE+RnLHQm3s3wvOYW1dOrM0fCJEQU5PXphrh5nPauDhzANADx47ty0/uR+IEoh2JmcnMhn4RNfTSN0wirOYoEYIFrOIa9IsZc+BAe7ZIsdrmah9ixQYClt9wJJFmynKXOHCA83fdg56z2tF9ncdKe1uPdttc0ji7zt0yMXEwByzcNPy8YodoQjSZ+kyVmLSjEqYOkKIgyVS61y2MfFkjOrLuVDvisTtKbKokysRGGpvaIZ9k/7o2XvazYRmQRCkKSCYvojrWhAw6eZyPzQOkwa+4YwOiPVw6c/o5j/vDKZx9yxbf2ToK2GpL9uO+pS578yA9/tJ915NlidYRy2zDFwcBGOoSTuZpGOipddWb6yJ1pmRgpL7U7Iv3u+ek/bkwHXp4Os69tebrWK2gdInNjut/viyDuvsULGqCKeTbc5uNCY5Ge6mA+eZuci8MCLd0rXXbhLR/8P5vXDs0jAh5cnxYu2HrXD7+25H8t3ft1p60aTNfenSx8htHuIymHpj/tk2636VicSSluY3jtmubrX9/3rnc1oFUOP8cl6JQQlUnzR5/CN15Qr0NPOy2tWFGs8ujxOvE3wH/ehtah1fEU4BjMYdGhOlLhq12fziwQZdUykBnUrrgMiRNDzoPi2QrB0Fx6AfH1SAcCvzRQR0C3ec1aiLTdZJpcd/ZUsgIcYVPbGjIIriQwqMkqpiGjlWD4aL4H4ZhpQbFRop2DPL5gH1Wg2gKtwERbJezjUweeIWMEFlS0OQy2q4wx7o6fTDYw00961l/p9XUs4PIq+VVt9ZE2UjXuiKzHW4+AzrM0y4Fp58AsTpp2lt6TGdIUbANdQxfTLOKU2afJkekmHURnhephaSgsMOXFL07/+q/bARCTQC+DC5GMSaYK+X6kn9A4dVkxhWqIuSaFmM2h4pQF8fAYIfdNlWjVTCpGk5rUXnBBUR/peZ4sQrFtHEjmpvHaTssiJqamrU8786DP/cvxD99ykQibbWnOQNo6P226KD3pXW/b6+AHDzz3mS9Z8OgHHnDL7zfcNfyz2w6/cPixWy/Zp/83BR+ccuTcP26Y8jvOcjV6vQDv7LcqUyNteWX6wOLCm1WEfrAFc9O2x6aLbk/3uSg9+ivpWX+Vzl2WVq1Lewkz9+bWuxtLnJmEV5mwIvqic4/4lVXjgNlrSfPrH7h+zdD+QJI3yshncVoPih049Oevf2PvQzYPPC7NE2Ub+Vt6s0LrxRvicUsgqUAKfWnryE/zwmZ///s6oqEUceWBGHINu7+wFibiJ/uB9L6vXvZSXt7KudlqYG2xc6sjMStOPFhfnSgyL8MR2O597ysimuuZCL0imUG4HdvljDvmXyeS2DJBPACQVoPsgVnLv5avCapqcyNVDnmC7EWOO3FqQrJ+evbZw5dc0hoy8LZqLDKcrTnyuVodUzeDIo6NgEImTfLkBNJAreAlAojLr8nDE2diWaG2eEex4BWMH3gIlsJhRWMdMj0Dm8AjZ38bvACWa8TnhIA5j5NqyIk/icYzRZHMUinH8DRCvaKwWZrlQBH6OUu7CwdoFvZeWChbQqHQ9fwcPChU8ySI3qG5KKOguKDOLB/I2TwbRgGPHL5HL1v2lhiUsfjtQboPcFGfaSHWxTQ9fOw5Q5qUUv7FLwpXFqKXnXspNCqvg7gJI4pOZQDoWfUx0Q/UyCoAT0JxEeON+O3f+57+e7/xhVe+dct9Nv2OyWf+f5ke94X07HVD8/Bz1aolH/rQKYce9ZRLf9O87osDjesa+43EqNL1eG73EytrTjx1Ek/D6vOBBan2gcO3H9K8DnpzB0ISOn1QunZR2nJk+s1p6WFfTP/1vek1x6ffzkvrf58eIjhpW5o3Z6ivbNfL151riI2R+Pabt8BHAZI8EiaXM5EAvOff91uaHv7o9COxsz7z0mZOuB+lJ+y13+J5dzQ2F7HJITSkYCACp3K4DBfgd7/beMxjCsmZHHltXAZJclBbPkWnpHIC1Q08Lw6r3A3ZAC+WCE6K3UDlR4TvgGKWwMrETksJu8DH7DSxh2ygTP0FiLPuPGcZbHnQTYtNmj/hYJSzeU7FjyuHkLRyHdpdGy9POblhC0XLfjeNgfws5CGsE6MN7el3PjNQz+Sq/hKbdgVV7pu3CGMCTPnDqAvzFnywiJabjDm6XjfZ+gc4Qjmcl3A5DzSlAfEYRPQVciiD8Q73mINxc4p/B7/kqUVyALNiCgdKulZ/S29W8eTzj/84DplVajj7dZYDk+PALE6aHN92iqdoDdqQH5uugQC4T2hqio+ipFNoEObWlG5yO8Ctj3D+wyKBLZTlQ937aieRgFNQCSgxPRViCRJRUrbWm9h5BDGS00XsEDWKaFjWArHWtGqZFCdIyIJXGSdFAvCljGCAJCkrZs9u+b32anz0/3vIpYe8avWPrvjBR++8YduyS9OR16T7BUighT/zmcZpnxu44up0622Faqb0EW4wruyxBOVSynXr6dpCEluFvUGFB2LLgq2vPWDfVb+1oWzfdMsh6co5Iz4eByAcnS4ZTFe8IH3p3enMBYV/baEXkQVIaVUo5OPj97ZJuE+U7vcHHb/o+1cOO9hPRJSsXMBMa9Nelmauu3bx+en5i9KGI9JlQrmBoZ+mEz+UXrLPYfsevrayWS8X5ELRhQhdeWXzllsag4OtKtjxHlEXLTT+jPIiz+HhBjvKvgLrFdI7YEc3eN2zZLslVSRNGoOL7QdnjTgHP5JPFcjIhkUHdIAAETMhJB7hOOGmEluTk7Usy00SbvmpJeFel3TmS/r+/XxiWXmgoeacPaLaASNrVTxJhDZ2qPlrnvO5z00SJwEuThIXfgTFRv3NQOgEvisSJWIP/KKLDGFAx1dDmOPHT/AQIee95pMzu4Nr9ZrRZNUe6lJhNYQ4XeAh5HTwwcWqIoQqAbBlJJIl+WCOO2YsXEpTXNjtksmzyfYcDszipF24r5kN257hpFhkoU2oGGtP4aNmHsLBMzmcRK/RSvRRQBNqSOamoSwEncWolO1KRFSYLocZoIWRZN2YqG46AAyiChkz4I+ir5sumUjTDTiDk0R510lI+Lnn9j/q+Q/49l4Hn3fettXrvLV+BAqNJaXZkUb5dGmxJIMacSPby7HM2v5n13kBy9QcXvo/P3j6KauuvU9aKTAoQBIDqCKAx73S+temd3wrfV8EeTwlBzUcTxV7OfL0+BS+Me3OvyE8zOnJLzvk+9/80+9X7+PMZKXwZll6u2nB4a99lUXGgZ+lh9+R/vGh6Srrfbelgy5NR1yblh+zcdTijmTcugg/gZV1x0+tLsUNppcAs3m8iRyHQqSBpJDGvdPtf5EufGi6fGOa/4t0zK9+8Mibb15qyabScGcRcTWR4QmpA8YVZHPuuUXOPiFgulWHqp5agRqoLA+ureRaO+OjMhzkTG79Feek4dbO6i+clgN7DyV4toMMG02kFwPbEcTGS8oBQyFIdsQRjYiDll7l1TkGJh56Hwt2GUehKyTwk6HNpYQIrcVfDmPDzXyJZwiU6Uw8ag4TKQdyGS+8tj5yxgF+X6cJxCGZJM0nCE7iXvJX6/i6YCMPYoKnIGOQyLPa4lf6hzCoDGBk/U7OUJE7PtEueZpgcDK5M0uzHJhGDszipGlk5g7NysTLBM6kjW6lJvzldKHO8sIBtUizUD2TI5r97LOLqaeFPGpX1AWQxI4yw3UycaRhRbZy51DWYVQko+9UbIqkFSI9ZcXm2TxFd9vjPeG8vF2h8lHDOgWkU/OFi/sX37tfvE9LAkYBRPaAUcF2T8mN7i4vssSDVoIAR8XJ0/oC7tlQPQlq9DVe8dnHfu5179r6+a+9Or1TDsVy6AhKYv6c9PiQvt/x9qgJW2vrL4NkYu1QHG/eqAHKDKCgn1hMG60RJtuouGLFKKQ7+Nh7v+Gcree86frf/MZSmkOR5yx66OAr/n7xac8qxOx975t7RXrAlWlQkMm2NLeZBlhTPoBSSI2CKh0fd5qPelSDG6YzxYnYvDJcZJareD5kDgGELC1Jd70lvfXYdHGDnys1vBP5C0PPu/mPf7n5kUsrsSn8FnoBiC9VrHXJHaSU6zGMtDTxkQVME37Tlg+6afYi+Dq8JlZ+veRHK/zFYdFsdmsGSakrbdEnRXFwq68t85TeU1YACVLL80v5sUSsO5oSSkOE02C0DZMwCEjPIMlPioBLyIaxHIjKTYXitmVE96Eo6NBiljuCvgm8WUTdU1sUM0LQGyiJzy3JEKCFrPGRT29m5JLMpCwgCeKRA36qjBKDVBIBo5xwnNZ0i4Fv36ITB+RAKnitgCqZa2kQ/Af2VWRg9LfZf7McmAoH6NndiTZs2HD88cdfy+O8u9NXvtJcvrzZ19ecO3f009/PMjUbjdGbj3hE86EPbT772c3DDmvut1/zoIOaZ5zR/NOfeuCLE3TWrm1ed13zD3+wVtLctKk5PNz68Te8oXn88c1TT20+6UnNvfYq6uAzYimn4a+sDjyw+eAHN1/5yubVVxfVOPzw1tn+6letq1e+e/PNzX2LAyOrnzlzRlNt2ND83/+7+qs6vOQlRYKtW5s4/9SnNo86qvio1fOe17zssnIJxTU+lDng+t73bv7sZ9Vk3X9fv27o7mtvGdaXUXU5jhUwdPgR11zT/NGPmtdf31y/vrltW3PLluYrXlGIwUhaYWJQUfUzb16RgTQq9pjHNM85p7lu3bjqDG0bclTSXbdvuWu1v1vvumtYtog8HHHEOP6o1A03ND/72ebJJzcHuOFGeVst0f6qk5849Nvfjiul/uVLX2o+0Ka9UgctXNj0yXfOSJ/8z3TUt9PJX0+nfCOd8uN0wnfSk85+8W/uvKOFdJIWFT799O2P53zKF296U70io3cMK4wyyjRtjOUT5BY5SxzPYvKDHtT8+tcL4TGCrrqqYBT+x6+adtJJzRNPHM2zcxF+/bu/KwZjneR/wAFFDUmyT2iDJzyh+bGPFb3saygKP0lz3/sW0vg//kfz6KObj39884lPbB5zTPO445o/+UnTIFK3MnNcH3xwkzZoR/SDBlYeyV+jRcp9ylPGDQGsuPjiIuecst0F9SWZUU/wiFkQHU8hqLNsn/a0gqXGo34k/3sskXbdd8EFhYy109V7LHMqDV+7du28efPWVbReJdHYV9OI3Yr2EJxkDHz234cOWrpp78aahf2bBO5SgnUtA7JUblLHdNO00/vf3wTLTjmlUFiUPgAXarpS+uS+horXFmp95crRuofyLWfozq9/PXHLsO7HP66yRT6ve932Zy+6qNC/OXM5n3BCYeSC5HD55c0PftCO9+Z55zVXr97+YFwxY4ucOlSy9HENkUwJwKsBOFPPF3AbT2r43OeOGvURp0sdsgzvtWR4yZICMr7mNc0rrthuXZgZHzl0oM2bm//xH83BweLzxS82fQ26447mq1+dwcS4Qp2B9dD73XneOSNQq03WClX0M57RoonlRn8uPe9X6eFfTU+LzzfTU3+ejnv1Xufdcv3GNhkXt/VpXWZytu99b9tHcUmygBc5/YQXyoKrApq4dvHXf928++7RUjZubH7ve8VImTCfegK4CpQsE74ZF3//90VuylLVKBc8OuSQpjmM8egnXwPqSYPJnjLr+Kd/aj760QVIevrTm9/4RnFzxYrWteqA8gF0eKte1bijOBdqZUx95zvbKw52P+pRo2Cx3bP1+2eeOSqccJuBTM4PPbQQ42XLmo97XBMK14Q9kLQaWv3bvy06wjSDNj7rrOadd+6BnOi2yT3hpDGX5VRcUrPP7lgODA81b77iruZXv/zetS/6WvMvzh06/fStn2lsHR/YPFKl+uKUKG8RFbFkMI21tpowOFhsK+P6toJjZxkf+9SJRz2W+SwKcNHzsXO2xwKZkG2LcUFc9BYFRGvxyU9IEguS8J4TgVwyV4QzApzoXz6HSWCW/dLWLKTk8Ld2E8f95eLcd+KATdoveMH2hc5ctGXBvHyQb7rAH8sr9VUViw7WmMRd1X8qP15U1w51B/YJ2FFvH4wWLRyv1ColxSi9MEYjCxhjX7b/H7kt+MN2RfuMrMtY4xDOgjOOfhCSb71GPi3J8i6OiV73sWaUg1fEkNlwZL1pZNFEAVF0079nPWXrz6+41wv+Znu1yjkriKzahWBdJq9JlROUr0eWvyLn4jYjjDZtbA7H1cjX+h99ai1pcLD+SxE8RJzakVMMNIdgT9A745/3yAgTirt4q4HWaq0ryeSu24f++PstX/70+gsvLADE+Ocm/iaT8vg1HHSWpTGr5Ej+qhpjxFfxOvYoGDUqow4eFOsj9lwskTtGEBm2fG/VzFqbzWVuWjFsSeUBUklgBMkzt7fya/DNcpj1shyWJI11OqfPx/p15ZEOX0V8RxQU8ReybUDIM0LCjSBBbPaXGH0WFv3NfOiQ4e7xk5B2K5sWMekrkWfUr2Nj3SmLyu7R0nukFa2CTe6RiswW2h0HKLvVKzf97r3fue/5Z89pbnbomvdvviK95+rm8h+mFd3kIRpAFKSYiWkkAQTMt81uoqaoSzFSNBe7OxWSDwND02lykPzd/Nu/LUI7RY3AHMJxEJDk/GL7aLok1XvzmwsDzzawOl4OXzefEIiDBpgfIREiYwTlQAAi4gVGTEhUVTsS+StoJsCfNLS5nG0yEgZrZ5DQE00rB3BU8/GbMCLHGKgTQyFezLZ1bBpPbti55myhwjCPgpXxKYpvDWHFovXVB2Em2+lj7ySiYf2kKLVSW2zvkvDNuZ1CW0TbKF0pc+c2RC99/BPzO4Rvw2Qi4exX7waLXJie9MB0udOttqa5gnmck74m7X3nAQ9s9rcGYbnmICXLKsRN8Fa2HyJaCACE147e8pbC5KhhT0Roc7dolL4CVuwF4w362Dvv+vTXF28ZzucUgUpd83ck3L4cgqMtxKHCN6NGl7mpmU61yOQmDhg7BDsIxs3XEksgCBqgrxMwBMvqU48ANxGhBXXpVk8ZgOZg5bLKORAhgiGML5flV0FjaujZPMDLj7S7hn6cDUE+keO8L7qoUDX0Dw6rvyhJQmu+JKDNcU0iGh2I1UHw2pWyy93XcIFcIGNMUEF/RN4Eq5V5vsu1ayep8CxO2kk6oqtqmB0Kcb3x4jvmf+o7S5tDt6X96Rla9t7p9mPTJT9MJ1aCc1tmyhCOGLCWP/Z8k0o1vTOTQ/QgxWQ2Q/3Zew/WTIVkQgNSxzQgTecvBc36cqbAExQ0hW47zKSJAu1wMiQ9C0tRx6HE2QDNfMMbitdZqEZn8opQzW/JZH4j7QrSNPCI8wmqQA79s71ckC/E0KkIvIA7fDqSaG6Z2/o0IhKQ5nZLzDAtX9445NAGJOSYn7AitpI57jlAUmTMAgEQHC0cb7AUhJYNf6VkDRFRyytmb7lA4GOOKd7BxwOnXayg/uKZC8VdeTC+elzDmbcu6Svp1KPTZQ9LFw+ljcDAxrT4gvSMI59z6KIlVbxYyVD9iSi/CJ8Ebuug2GrujbYdyIFAvYIkuWl4gBXXRMhXRt38ZMSBBEePCcFowb6OdpCUWUJGfxz/Tzx4HBQZt6HSlumVXr/vjm7V1/x2ZYKS4XX9ZZQ5X54LqkJqBXw4mtW4YIzNBDgwMNB0C1DnXoWiBgdb4CSSzFvJxWVyoqMJpMRURNyXD0Ner2el9MrXCFR3U2S361AOvupf9aQT4ANIDpkTkmFnXbYT3SLRbkEmpRCk7suEw4bkFCerObc9/GIWJ+0yAmDPiE3F1ib2v33jmZuvs8BFKxTKlSc/LT0x/di7SG0+mrA9Rg4bZtpx8MHjxtWED1YS0FDm2bQ/rWr9hbIzMq1D0Zv8+Zzqdu32qgErRcQqm8Fv0Y0NZqpdWxewMAQnzSjRsJYzCgtXeGSKU6QhQh4a6wvOruxMdrdBP87XqZDeYiGyLgN5nWweICmn5L7CQGthUyQFeWWHYyNYpm3bGuyEo7HF4j7/9P4HHpEOPbzBsAE0YU40U39VaqLJUKkPnMRv5/hpE9Nc+Vw9AimNJS3uAV4ZbkUv+4PAvI28S4JCwMru6ZZ00LvTq09OP3Tk5tY07w/pyLuOedTbX3ivvMW9Q1baa7MY+eT45H7ABNf1RpVzACV7JYMLILMIEgte+l1ZYxR4yN8KVCp+l1IrmLdA52OPjPsPAMXKmmTSex1KS3KgVJyhWvlVt0KKRlBuNcE2kEk7DORXiNZPlQrgG5Bk+x7PkGSKBnFAHyDSeHzd64o7ruuEw9xd8uftcCCCfIwgi7a8uTxMULiVsl4JPoOQ5Kz5uRUyUefwUakbNmIU0VJtA2r3PilArxng2mvPo7bHgVg6ixIwSwkiijoOH8pLn71yfo9NP4uTdo2uJ+UmwUJwDIP+JXNva9znwOafh5qN/sKdlJy9sizdOS9t6gYnUayW8HnIvfR0QpdsbNw1ATUIgxjX//JfCtDg8GthDYZoJpX85CcLBGOpgmeCwrWuNCFRduVMyukjKETRPhSiZMJu4LxcmXLi6b0Wmrpm1dYD0upFaePW5sCdaW9vNPv5zxucTKanvDWd3+Bmccf0ziu6ysQTwN+WNTseWoarEB463nPqOEm2TkO29AAA3Xxzg3K811LnIDUee2LjvvcrzTrHimfI2/UC9eqIAa4XvrTyio9H9TLs7nTBMKu+Ok+ctMTedZYsrzCOldPiv6LzuZotfm516/r+wY8NL1/UXONIgsEHL37tP/Qfdvh2xrZ6Yvs9VSL2E0p+fqCCGPL9Dhc62uneJjZkANla7yyiESqNljEHUvwQf9UNN8KN2q47+N6MAoKE28ZOOzIGW+Ik6QFiUCMWaAxwMNewZWKtKRNOpxaZ8HDS+En9FeG+WgHExMmgZoD9xDmktv6aUbgYHBx1oOYqRf0JHniEFcasTGzIAsh++cuG3HgZnSkFtLVracumqaFaAV5cYjrReRwkHEOiMhxjahtNAx2gJSF3wNNujJMoSRwmFVpKpeggcQiUsI8ZC27oIzF/PL5GGfNhIAu47F7+W/bCnnZzFiftGj3uwD2axWg3Ibtm9b6f7HvZ8qFr90srj25eep90I0DyrfS0DWMnDU7YJIrJohgHuJeQtCPDjwuB3nGodznsgGIKFWmiVic5O9ZPqLhhyScP05j5UZHMP6orRFrVp35fYvdNjJQVIEl9aAGD38pO6MF66dN2x0tzt65cnrZ4/a4IsC0F1zc5Cmr9lqUUNJvHty80iJZnUVqS++KZhE1AGD/7WcEKOgv6wb2yIWnZcDZsWkhBsQrGrqjPPO9kG39yZi6F7ZGgXUdIJgeB7dYTKziJY5I3sYIkGFooQbiYJSHOG7zrTIycqCz+hu6JCWz2Dazdto+aH31s4TCIOXT3OcxoSi4o/iSRQDjD7VrCKxU3UsCmUbBDWiwScShCFWCKFvHk8YjAqWUiM04zYhrZvHaELR28a4YPniPDE6jl1BRPDWcAQMiQV4RBh8lEQlYupIRyACzJjGU3DUaC6sIIFWDHOZ0Fu8hljCxt8yvzndnJmKV91aqmZl5zTcP6rBEt8y5JceZpmGNMiTrnC+S55MzGZAKszphGSapwkKpydFk01yjs9eDuR9CPlfoA4npHS81OiR9ZMvp0CmVl2sZ7HYQ5rnmaO8Tk7X5cmmKLZnHSFBm4gx5nqGgl2o1SWLVqfmPeMVduvP/S5h03pYNOS1+6KJ304fQy3paeasNvcdZZ23VK5VleE6dHZtWWfzUsvbekwxgTqwTSUaPc8ipsntpBDxrGxm3F0OayqDlW1uRVNaQBFqld77KlW2eCFMTwQzYLN9/x8D995z7NB/0sPYSLjrFQvmqKOeHhZk7Mz8TPlrd61evDjFlfEO0hvVbgmLl11uDSU2q+Uu5lwpD6kZXlBL1eK6KDycy5mYYWYLANRMN/zK93k1VXk/U6CV6xRklWWVDO/86khh3Eqd2zGOXDLjq9s1Pke7vnZ/I+W4UYdR+y2llcl8zddPoZc898ST8mWIpi23BPDgaaKUrL3X9kBjIwuKQHeiiHCpHMDvAUvvesTMx/uAOFaulZws9B5Sauotgq69pPOajOV2gJuelaen0HoPhLQkJXxN8i0cjbWuxHO//85vCQzfryHUWE8aum8WoIyOswW4uU+a/ALH1tymRMgY+wtUwoHGDIffAUAoAY6A21AhdIoAqLWyfeNmFwQYGeuxNpqWmJqanu0OOYTx70HfAqGM4dv9qckUGStutiONvMRGzALHXJgVmc1CWj7uFkFAqnt0kkndXf3+hfNO/ugf03NpddOHzfizY8+bbm4i0W33okCKas1MpP0z4mYe3IHDcsQcsE5nODg0XONJThSoVRwchI9kHlQl3TevFrvk8bGuomr6GU2UI5SGYdh6/eDJKWn3ai+i2TmZnBCkenG49JN2xIx1gDGSnIvL/P0l8xLx4jQFCgEl83VcWe0chUsKActc2knh382xJDnBaqygTTOFJ5xxOPl6rCPS1JxwnjrbM9d1n9KT/R0bbliVhitDoQGG3pRBEdcqs8HjLjJmMgor3M80rKqX81Hee66IkYfoF6QeDyeMePMRBS1JzTGHrE4eve8YEFxz66f+GiggNsGGjlGHdTFD4eomW8tyQjwhYniErmFZzEOlp0A09bspQd9StWQ1of/GCx8ouZPsZa8D/+ehZVeqT81bU0nlJnggGs+Fou0YqPJTlIbtH8dMcGOwnqtsZZbg27DTzLp2UAdiaZyw1RKS7Yfi5twmOZic4xHoUTWFHy12CUGIzDQMrE/AS7OD61HSwzP9mliWCcd17BBF4ivW9/iWaGqyy6QHeYvUQzxeGJk6sQFdeT+7by+B74tUWkwh7IhZ2/ySyBtyNRiJAHDWXYDzf6lx86f//Dl946sN8kQJImszHtVEbW8i05YzQKj2hJfoJjuLjpJureKKVGg1Q7PuUH3UHhWdFGBpWdoAJcR93CclBwPtZ9TE9nyHnOVIg9CoeKKJHL04P/nA5ckDbOTZu8nWNB2rQw3V2ZENPUAKVw6TPOKP6arVpvgpyiUeVmtrym42AIz5qOh4LTRkpwEs6Vlvn3dNOOtg6vE1ElzFe9CoF6nSfoFlZIbAdiHVk4aQDEOg5r+WC4vngrBSNbPhA1j3szR4aJxdZwDsVfRlptdZ8wrJbE8GdSN0uucfjC2M3GnIHmf3/Bxj/+YdtP/rDsxCcvWLR4dFDzfAiZt62SIQSD2D+y0ZL8RFbNWCrR90YNzMElSQhpjDo9/ekFqgAvIpKM2OuCDJJyei2dkELONdDjvF++6hry4GNKY5sbPujZF7xQTtsnGOVsPShcBlwzZ9OVEE8HGhwslIkmq7BONznRCmJgLVi5OsUblgwoC/0apTLceE7HkAAPia4uM880PDuTB6NdnZPdI7/yotEwZixORrBwiSG6GB5FWc3GNf0ZkqMtLQFoOf090pZdq9BdHFrvWsyeWm1BBC+TF83NnBjzdAoVQL9QfPQ41dATGUVvelPbJzprSQpI4KclMHqqTJSjd7ja4G08ezs63wxtrra0YRiYDpVUHx/pkb8iD4BCaz3GswtqzjmBEJiGzxDRO5lWpv37ijeXqdHQnOKNH1xJTV9NoXOauOBpsKPEnJWaZhJsLmM+RQZ0iXVoOqaRY9wKCJgiqxk1+ZXKl7/qO0iIjVGTrFi1X19z0XPjrVgxqnnLT8HTPEbilNtRB59lPEIwSDJ2cZyQkwkJf8S7cJmwlGEhJnxkigkwQUR8S7ti6ccr1SruHMUBRmWCtKxxQz/ABMLSl76078wzF+Ez+DA8VCwhCaa2eiJaiISbCRg1BhE3GzunyV1abinx0Dmf9lioMP0AQORBCh4BJdQILG65TeRvMLyoRg3GtGxvuVHl62LaNuINAt3UXCku+DmgFvOlBx4+vM/c9bdtcaRPFXzB2UHqZtQY+A6nMP2Qm7YY9WRD5hHYbmSpp0AoukWUEvUSWiVygMkIG+5xpMmHJGOgTDKZg8kqcyPfzxehaugcWREt/ns9Vc4hp7xHLlQPeqZdy11j9GkUbXnddaPDk47VBXkjCAaKQ614j4Dp8uI+MfAUZmLRztPee4TJ7QqdxUntOLPT3SfB3qMpLIZAG8D+MsxUqr21vPSmWT0R097Bf0DRO4OxJamG/W6UiL3cPkYgrWTuyOSbF0YwCu0skFARdCUzI1DA2DZ6XQRUctOH1ouv8qQK3XGBTIYYQo/76qas5G+L2eTeI9uyFfWbpqeZbkkHQHewEXVNtY9c9G1KApJLencsNa0a5kFfaI4AdpM8ej/aMpaq03/ssln6niW1tX9bt9LFVt+wXRfoTY5D8JS2bUkaHj3Y8lc3wS+C2pmYc50LcLTMSsVIThCFrmIQUufNhp2Lm8ZfCSonIrEvmy5GC/bNxIxZAAIEAXF/NZbYuwOPkhxyYlBALcSPZ8hycwQGwV6SBfjIWXW+CEbhoaeATiqC7ZQJk0/A9KYLwU+6w9oTtwoYgQxeaaZCiot6qoDe4QyOocToaq/ifvmrvnkLBpZu0TKvgMn+Ma/XECIzbkBxlpgK0ioWzqgOKsWYUj1Pwkw2RhAnExJMgyxl7icf5aoAfKN0N/31lAt+Sm3UR+ogGW0JwLXD1nqQ81vgjqD7KO6kkwrEuZNImg6yxkr561/tDdIo3UfALN+LqsQ0XznzKBNeSQImpSY7mFcoEpELclPTvE4AabUVOriZqICY0Cf8hHuzVOHALE6qMGSn/iogBkax1mNWRNwJNBvGt5wNSfe1ZwtDu7V8hOLmo6rMvZSoOPAlTruBzxhRgbrUvdhq1+VqUM2UGj3lwjxPMATtIwc6S7JwCzN7ijBWqTZj3oUE1KtfGWC/MttKsW3KUzNNvClaHUSb35gOGIkvH5iT+LiGndMD6bXESarNn6+qVJLKU8cqvyuSXhDrqh9p2y5nlqwve9yO9Ka+q2yRqydm0pyUrVB7MOtwPwSDABBX110eiV4vpeUdNsYnpLFlgs43yTbjjW/AseM2fGWBoOQ8EMi/6BCTftBBE4xfOEB7vSqEA8mIwBxDQDLCo+045ppEaekkiBGNB/1VB24DIMzoUwQraDVc9T784cIBpqqGrYKmkRRK+JELDVEE2VANB7JvWj//fgvWrF591+qhRcPNvr3nbzv1tHkPecQADjD/xk4mM6JYhnNqCe+s+iPACN9sidCKIHcIKp7LQRF0hUYpjvsqCK5y7Vciig+ERzfxSVNKLUlKkVJimEi+zDXERgQN4ZlzZ0JSAUDEX4Uquk5+ogM1FmeEnGuyiiFdpiNUj1R0HinEw0bIMskqOtoMB+iMA1Fj76dQS4CPyNl2atILSNkhSAiVIgEUxS3nWXCZAw+fjQKkYs7+kD4LcLm4Pfl6FiftSr1vwNvTYUiIXTC0yDqhN9dvOZLNnIwTiibGQKWdbINh1o78aglcjKf1NST/cOdYgICTMlFq9dMU41eKwOwEGqOA5BaVtIJDAZkVhYIO5SVPKtURuurDDy9PQeKUjpt2VtN9AV9MdNhI43yGyBZ6uth+t6DhNG9kJaE5NBp/2p5ZqdCPprMsnGZyetGVHXg7Q/WfrmzVPDR4NxkSSN4dhqG+9kTpg54W5loKZzlzJfLBECS4n40EmDLhJN1tIYbAEGamkctBoV2SpzgJTKzZGFZBDgYFy2F907gQZs4bSthMwWEIAiBBrwTfOAFIVqSaFbcklEVU6fLnJzPRx1LSLoCGqROrxAZjmnFBztkqvNIoDTdUJy05Glgmfh1iqdDIEHZnOI0pzGSbjcrgc+Wpcg6TvlaijxJJhR5kdPe598DjT1i0dN7Gm1duXLxszu23DT3gIfPVh3o58cRisZ6jK5+IqFxLbA4loUD4OcgPjUfRlRE5UfEI7smER8RXaSBCYAhpFOaDRPpUR2usDHlZnGvVTnhkAkn4VYVR9Ii9LFAv4exMvHdOQhE/TgPwgakqcVIBeboDdWE7l7+Do+hDnFFVr2SkzXi86FhCoqeijbZTtBNCA4F6VCWtkwmKvsMWZWGFB9U/wvPxLYiHDDwS+2UlTkHcmeBmEPTsQD4Qn3CqpDqoj9B46wyqMUtlDnStcsoPzV7fcxwwjEUYrFhRDBLyHXpBtKy3dZbJT8Sd+jBQxVMbsRXy8rLO0xexBSKfzMAUJKXR1WHrViVzX9kM9aSszZuNYYOZNuR2YrGE43DAMAlMhfgeEdASq6TBDwwxWtIbw3411zHD4xZGMJb6qFWH5cJ6Nbq/Q8t4RxUvGqVTok7wKCdTcx8WV0PoYspoDyGmhXmgYTMRPMDXoRLgAuEpG7+cpuWFfud8IrEi8ASX6A6Pu0PgWRcaPNAM2xky3zKTuEliAwbBLo6oYIoYDzfladXGdJkZIGB2Y0EtcZ/REh/jp0mQJnvcp0KEX+ksFlbACgpiyAUhAVWkhZCoD7Bi5iClv66lQfG3kluvXwMGxVOgho8i2EUKwdAj5NNSSqVWkWcMB8WZPOg+sGzvfRpPfeaChx89b91dQzff2veOd/V//A2jFWC8Hc0Fa4LUuBQkBwte1IVMcIn+8bdCcAPnuqkUf4wu8BcaICcygQ7VBBaRZ3CY/gGbJGtHCqJ2yiiKKLqjy9rhJDyUeTiixMzpRNWm7ngQlQs86Wg6wRCg1oAkucWcQQIKWXpt5wSK4QMAUXT0G0XXksw8jQgFeTb3HdESlsCpbzIZIeqwqYoZIzFMXBuhALrNcfRnmfBKAJwm07Rqjm9yM1KseM7ipDKjXM/ipApDdoGvVIYxXCbn9FiMMxUQVGR4UMHsgUmMMSClj+Fa1psQjA20nXFSqCq7sSZHHoerrJpx9VMQps70FFNHR7vPPlFA7hiQsYG5rIxi4uitqLzHxnkQFxqARXuKCAFHkJmx2RI9RccpTvBWN0Z0NLvaP6qH2qIsQilTtTRO1ke15NtvYLJPTNw1xxS20jvbk+5eVxCA8GSuEYqe1OGVD3ej8BegIdR0Ty3GPTvImDe2XIeSFs4qFzwHupgIZTvaOVsm01gAec3yZZWJLEFgwDdzJVsjwlAibP6aVZtSWxebRoxL1IXRyFy1cYbhhBuIPfsUhTLbZZsXI7Qbkcst6uZChlw7Miei1n2ipzR/5kiT5U8qFIerrs2RbPvYe1mfzyfOLU4+LDcTl+LdAJb1y6SqoQrKN/O1bGVuyAdpI3tPDULVwBnvEdQLoHQph1QKdeRZFQvm0Jlwg5stiZPyoosKVBFwR2OjpeTNJFBHYziAqOthEaidABNmnEGq5KaIIrpLAg8iCcw6pLFK0LLOaoU/XhEj0C0gEaRovCgFQHSHFiLYw8MF1h6Gg7kSlTWnIQG8TvAqUYaGQ0C0aK+nVI+QSD8h4bbKY3iHDpowk10owSxO2oU6q1NVQR+BSnwzxq2FdrOTLP2keXCwMAACRa07cGib7gNSOUGnfKfwm1FnLqVEYy9Up8xgCE5pTmZD1KhuqRGiTKYug6S446vQKOrPyoUzYKyRUTfycV9bqEjZ8uTTbpMg+lFu6skeB9EFlQrUs5U+9KNq0Iwc45o804ytV2MSd2hzaxZkw8KHHplEnS2V2p+l4QET1QEPmUauwUmHpeOeT4XgiQ6egEpi9QF6bOYC48ogqZwsEIk7+lcaizJE1DTazBusny4i3gYdniiFFWRTIW8CzzQSLQuCfoKl/MV8CVzMELF8yo1hwrhSEWoyc2RQII3CZ3MkblqLPjEq1cEcSasr5KxLzj8rVgRyQsIoRjqwgllW4AzTmxUrCh2IkxoLKhnIUZMJM5SAtwYKgZPAWToKfzDNwRN1aZSY5HstXXg9gXI+cuBGozwLXZEoQwDACsQjN32tx4P8atujp9SfVJs0Rj3VXw5mg3pH5VsSoCPuwkQictNM0MpSteKipVs3bGk2LT06F3dUnIa3umxAgQZphTTN7JTMaymZRJysuqmzM0mf6imTEK3QWdYZvJmgm16rlL5rfa0xb9eq/mxtSxww1H1ofIOQa9d441JCBqTh5y/9Yv7qV4OhPmxKOU3nJXVpPFfIsIyRWblf/tpSldM49CAn9lvfOm7eo2n0r494Akt1k7D6HonNemH41QSXTMI6Wy9PRVmeou8qYRbl5uw816oqykfQtFkvMdBqm18sYYCJPGqsO6cd0OnDo2M1quKuzw1hEiLuJ8MOP3GW8PPdg6QyFtQIvEl8N4Qb7BMz40FDZhqJceJc5DshRawjeVYWiyKkSciIQolWyI+/AWKU3lneJlc9ecofGXQQBtLvcaf4Mt3E7moRlirOKrkgfSAghomiKKiWRN6s2HKodCaPW0jlWtbFeEhE+VScvABzKK7iMumcVflX/ULO1RPCNp80sTQixEfXdZcK8OhQNfpRo4gNBOwRMwTyI71a6WswnaaF4QIrw4uWXGEmj0A5/vpQZSCOmis3xAPT6iWW60mlh1Z30+Oi+pSCIkM7/7iQyul1fV9f8y//sqFFFYLGzAo4xnywTq+piRDvfFhDJb2vwXxhHlQHdaftMJO2i3bvXO16VrvWnVmctGv118S1NdLM5uEJcR7GqilCnNoS+tcYtvfBXMfav7Gxy5HW+Xg7vVHdkqzET45ka+XOMj8oSWn6inX+Ug0dTJeUkiFG0f5b6wszpC/Cqk1L5rS8I79JAmK5TeXBJqf7hMIt7o4Rcy7egh2iB+sWSNu1GjAqk34JqfPTPULqE9CkQ69VKqYHGSoeVmtD00g6C04SCGiljwcCZ1gmuIE1MiRDgJk3MuZ6xJ51krQpVkz+DBvob5FLBeAkRq57FvVaOrRNADCWMChLMzOVr/NNF9AGNDkhsdDWnjj/oA2DwgW3KJcVAy9CHzN9JiQPQi1C7MEX7hDhRCqpd0R5euWLnAGaylijBziQzC7MJTxLhUogH33nY8KAPCUfY0objS8TCcgJB7AaQULRQIqXlx1/jDg3hTHJQf7uExjXXZLHMU1WVJYiBoY2bm4y6Npf+JByJtxsVifr2l4lRXeddVYxsVQHeM5GFok7MBDipxA0ljYI/miRaQmIOb1jJ1d+J7mYxUk7SUdMZzUMeIoDWmIC7VuuE7Nn5HT2r9af2sF3LAZxg1dIWJUZZGdXAeVFW02CWEpnGYdHPWCErNoRvWMPIIRhlZNmgZCsGlR0a7tne7p/223NTZsaH/rA0G2rhs96yxy6uOVaQJd5apeIomhdfiSgXv5auTB31zQxcBVVS6dHYGklPZ88bWuz2w4gHcRCsDEMs05n+L0zxP6ynsjjpubsBBjRkmSrFGl0NLTRfS+DX3wSorWsEYvlEsyh++Sj2lCmEhXNDxHQwXU7DNGyVj3djDqzrIpmmxU0cyBJxRh7zdR8F9wVAr/ipsMhQY2WBHng7YTE7cSTBMr4ZEgh1sfHuODqsOUwVuLaZQW+WLWXHh+QtWPRUSY5hNxmW31Uj7nR+4LNaQZFhzCA4/wuBg7wFOS+DIO37viqQ3E7Kkk+EQjlpg8xUIpsw2OtO4iW+wIueXOt9/nqEQypDLooK/761YeDUBgWme/fuk02ogRBJH/HUjZuuXF4TXF40jg/U/yqXFbAKbJdkvaagatbHgIqQBvzrs3ipC55OJtsJ+IAm2GDuk/LMWCKQ8vseApVYo5lpJlOmdd2MAxxjIpgSU8hKR3YD/9FiHqHyst8ckQleVkYBCnAxd4TAVK0T65hVEPMAc0iJb3AM2eNyQY3hOFUp+CPKZIQB6qTeqVGraLecvPwf/3rbT/6GR2nVX2fOGf4iU9sfvzj/T3tPSxXSdP4yXsl59RhCytSJsENlTvxKztUcTKVn5rKNbaAzoHq9AtDoiyTAcbJeg3x4CwBRyZBOtdE+dxziw1H2QZEPgo1Xpwx8+MfF0IrioVPCFivW9N25bLfHBUcIRxd2XCGB4vh1ASQIsSM4ZwhCrsb/gxFoxnVAPipLQTYGNe6oAhoy+Bm9O7IPwPK1ldQaUKCS7QC0K/nw8/hZEg78DmcBger/Rg5qxjEc845Ra3wBDRxh1NK1Kb1LFsQrEHXXaegHrmyRgbdEg/IgBAKjo4c/DX8c33ipuJ0rk8u14M+SiQDFItkOl3pLsIRhQna5ZDSiC61IG4h0pTDry2JNNJXoBWXkjxX/3mguXXUjRT/RhTn8NOfmR5+bAuQ1DLPzjfVHwfU2SckVgPVX012b5rUvHv3Zske0LoYrlnWd0yLKQhGjrFh2AwtMdf0ERPiUBz1qRPMIVZAHFIod0NRxABrTYnIqh3J0ASr84Sy3bNxn3LnKGJ6w5DgUpkANTts6Qhq0XZcm+8iGZvt2k/mdpMm5+bR4LYKU+Jmq6Jfv/yF4TGQFLk2vv/9xhteP/zudzcOOHBUJ/ZUHB87dd8rAQoVF1TkoFMo92wh4iaOSQ8qUd/TSAykYAjrg6wa0gXy55JRAcaYCAlIh3UmTeApE6vrA+ySMVNnskr8mCJnRQaIsQ1T7Cq/prUGotIlEUgmXM4srs51IV6QccVVfwkPI60g1s5nJki2TBqmYVSItOuKbE9XuXJmTZGWAvQgC8I9IxeH66QHeSvNN7qBnjFDaMclbbTKaTWKY2n8O/VGi1UlGMVyG6qoEXqDxwhWswxXgSYkQcfpKdzTOhXWHHXAQJ+cj5/K2dJy7kRVjRHJKgwPFBV+Iw/6VTJrsvK3t8BuTcthHoeWWpL84W/VlswjBxyw4Orfbrp1nWyKioTeEod20H7NvfZqpWFbZtrxplqZjWCFua5Bp27wIjw3o29K6FijHfTjLE7aQYzeqYoxMo2rGNU9VYyWASAo/QqyMWAoDtn6S5vIvE7MmzURIMDADgWhAqbmzsgx5MACdq5CrKD3GWWiaIQfCZdhhtuRunlEfKLcJtHAnC3vt9L5JypECVFPyEoK97j2BrkQ9+MRU9J2yzejSdv8o9zNg9mS4K3lGIHVt9/eQsF981vNU05p/tXpxU8MD8ZSr9iOhxGi0aaEIrCAV6OirNslLt/3iK4fVcClH2KvHDVdISEL4jxsq5wuwl5nWAPN4UySrcpoDjTGvcfFKAEoM5UQchnKTZ5ADCG3V4DjARSumDeijufWX6S0qE3mu6Q40gL/VdUQiGML4CSr5HhryMgZvICW1MRnekmGbD/RZeFcE5hpLyIqbNARRR9j3BQCoBTvjDhKyXNLMlPCyfIBti2TxU3+Fb5DodwdiPhZB5esoqY0mTPVIlG7tusCR6tYTa7EJOimQLEapWm4p5k5E9cBlfKdqJuv5TuRJlfbT5GJzGWoaB/XwCI1QodIQP/wcXJhWt6qk8fxVgAcxpJGj1x3VXrrv2y5flVDsJMKzmtsfcIT+o57zLRZeXXDf6rJEOBUI056Daw0AHdvmjYO7t5s2kVbZwpuBNaJGeC3OOus6rSpnjLfMQMzWbeZ3PCw6CDkwug1UCl9OktgowQUMV1gls/r45P3ZcjE4oLX7jI8ZcXhmofJjiokggEAOv30qmrLFXChFJm0A0kqw9hQx6K8DWMn19G/kyb1ceCbmM0KscSUFw6Ib9D2MmmO2HnxKBGNUf6pm2uG2eIOyBgok+1kjFuSKJPrri9UqkgjwUPmnYrWfDznblGBQHL1Zy1idjYw9UfyHbYHQ2jwMgnpcLOOkwiY6k0j2b8JsmSQlHPWcPhSrcg5E9hNoRgVpi5nEhfuy8eMWZ5AMMwaGyAqyeIrUyEuTSgMbNolGTvhKeSF0kcW+NhyowlmuvDCYnchMwl1AfqaGcNWlVRmWkg+YeldGKf+ttQMUyxLhWUO+ZFhAqyBHEVBilOBOpFVUmQ/f5fEn8RrG0ve7R7ROlqCg7CygmbggKdGWQfyK2VVJpqN80/9fQAvox5aUgTyNy7K6fN15afy13gQuyITYhC/+gvuhF/Nr/y1lIAq8dmARHVQjnv0rW22o/SE+fvtv/XOO5qW++yom9vfd8RRA8dM06KbIlSJd1DoIdUKmelrw9/iu/u7N83ipN25fx0mVBnz0Vqj0fKB0CWC3g1R5RCAN0MBXp41OEXn0Hq241oac8qZ5QlKxGjxMW79apLBo5DXrZnzCkiqlMuKUH8yt7G/HcXpz+1+9azmwBm0mNy8xH4qOImub8kcTgvtirMQ6zXh0jdf75VMuz0leoMKDnAJbsZyXpushs1699+//9/+bTvu0XytfvObCysFwrYk6l4PTo7EZ1DZDv+lJfP6CLNRmbJH5nAkez8V0hy7eGQOuJA34WIOemlJMBnxw64KbG2ZmHySSSmJa4Xiwj/0AABAAElEQVS4kWx9YFktslho6wCS4kGbAXkmusRJuCfuREQL4dTFcLyDcMAI5A6gwPzYhKh0AozPsTCk4SQBK6aFDFgAUd/JEyabCVJVReCkRoGDHK5ZVIgEtQDsVpoD3FuwbilFLWsopUgm/JH5eMKm7eY6dFE5gSZjrAp0HgL4j+2ZPKWj4xjSCBzM9VdEXOc7+akuL/KDeiQTfEk+w79oSkaBn312IRiERDRSOOdy4vrFqaeVnfmFy3l6CXN0pc7FGRzovuOmtxo7OLdZnLSDGd5DcQaPkclRz+Rw2lNAvRL70U4hGorsUGW+Vc/fpAESMt/lh3DN/WtgyJMLgS0xP3Oypbl1kGHvI5lfpRSBxKwGsetZKYzeqv2Tj5VvrS7rqXIqynfCTKTHKH4Fjh+2M0JWy5lM8VolA3q27A4Tvp4Uh1mv1TpWk9kWNqTCar5gQXNgeGioMOQUdmj/7QZgpP59l13WAIky53OjgAkuunY4qayO8yNdXnDUQbokgZnhq4vmA9DUep1Cv9fvd3lHE7TOX4qYoTWZJlTtKk8kVKNLIlpGk/U1YKUMrah+fk17y7XLOiYANCEplEsJaG5HUKmFZp4SyMxcgrkN4jywhqhd/I4qo0SbA2AF7YWVw+kSPsUYCxrYru3tiq7c12qlyMRfIhpLb5U00/WVM4xiYdT5hsvvcjHrsEpLIZQ9mjC9HaMW33sig1pcDuG3vWAsDicyGEWUjUZDmCCEXSZDzDRmwk0MwsjKIYbGo47TZRaYtCsEO7QQ4URxjbG0YlyXC+3+Wh/JUB/R+caalSxV5SuFSAwxsuRDaTu7ZIozkO6rVE+pepgQZz7F3sB6mt3yTu+2d7dkw87XKOqSixhGMWaoGDNdOzIqqx4T1poSp8rZg5bUwaIr3SqbsGIvhGI2zN4Cb9E1MZ49q3q21DI5FaIsDGxK38B2Pof0qEtLpiAoJCs4xfEh0Q48Q3liWimu/JU6CwqNE8amnGDq1wxqhB9xBqhbxYCxtV2GWagJjQMkWSUsZ9JsDg1sWNdM/RAXb4LdJOK+RgBThkox9+xr6fAI5uuseufqlLDBEzGBsclT81xoIYRMLCtoTYof0QcRj5ZuHlIxCb9aVIwW9rqbWEyMO2K20FSMUOTjL87AeQSDOPEcyJOMqarB5aONAJkVRo6HbkhcOXGt2y2lCI5h8MgzSGQIVBYElQt0CibL3lNeWG+eNp0wpnjIVE+25khEWm4x6Fx0SWUT7hFffWSiXI0VBUXkNHxaWFqpkjxNn844o4jLrqwTAUnO04cdcVhb7JfkZpuc0xf0NNYEODto0daECh14YHNwsFEfAu6EOqqkz1/N7tQnBnjcxKjQn8AZ8YCHguRDiqimoKlzUgepnnyUSPwgJB1EKkRKKVTphp7AA3sIeODuEaJAdJyADQhVVaFbXSBYPoTtHqnSDiu05GHcYWXu2QUZCXREfNqNLo4ToSQsqCkFjWk0AkzUaLv07Thqg1gHj1G7nwwDm2NFAFgI4Oqg67NyMYbVzQCmO4zbOkiKmkSasvvHrKgboqd8MEccEjNGz4qpZJLtYnPyStZKE2aFUTDBJAawB/m0OPY9y8LViTXl/UYcHsBruQgqg23IIK/+bOUOAGpXDpaWaNi8dGNaIBqeUVuYNhyVLl+UHGQJuPAv+cC8xmynYYvtGFgnTj7eoI4iFAjJoxkejd5hlnSHxtLXOt0yRBDMAe3VKZeideMbuD2t+6RIVuScAVDtSImxZZDkAbnlDLc/P9mrnJvmhLWj9JlGVtBPLIGzRtvVuVymZwECnVghUmrMMnV+laHBUgFJkR4bTYG0Oj5KtFzF8AhTE7rLC6UTPeuDDIpytF+lxJZf85j1q8zVSla6j3V34VfNL0tvy0za3ez8rG41foWUKdR8SVSWZVPX+OzMLbAGQ7Sa046HqYKl2pVYv6+/fvmTLRd8c2znfSmFsuo813YebvE0HaZPlEZsqj3vvAKt4ptSQF7tNWk0gsDoEEU/GRGZdFbcz3d6vZADnshEWSZjoUYg2kBmbrpQOqG6R0h7KUb6ikkyWAikSQJ/Hi5VSBMIAK+hkDsOPA/uBjQGj3eDpuwKTTAOTY5pCjEQplPOPzWnNBQrRM5s7CJwAWUMbOOEkbNMw0WPCB9lZ2j5yfjxa0tyX3Fwhjm6xJkoLBNZiqNOkpnkCaRQNFWuDsoqP1t/pH5HlYRbaqCCgsQevfzlxeS4Mxl+WmRZp4KKMOSNbywWH9XZdTckWAeyZHjK6qzzgxrLRQSZUVgVwkmGyiHmTu/NxKbildcyeFC1zW7NsfAqtzqnrF94pF7KkrR2c5rvfN1FaZPgBwf1XZcGvdqSLlLCSCbbgw8YGDIgnzJRYS3jPaUBf61FhqhUnirnUL9eODKTLt8P3e0OBye21N1UhIfYiIJnIzEEZ3hHTI5xxlcNZ8Y4VKxxZCZoDk+D5aeAF+Xi6tesCA+lR8xxe2pLZOUR1QtWyEFu8W4WmI/kq5ufOmerIXIgjVgRSzkEIOKXBfh3I6LyJ59m59yuSmQanSvtL0ZZtHIQojfDmx2pnpx9uskzM0rmlTErB3pGtSkQPjDciwYSIQ3plTozR26AkZFiIsFSap1x7Vrn8o6UAVyv5VbSr9/Qv6U2VKXRLy2VG42kDjxq8VKzSm75q8rbpufD+0VEoSs9UsHuEnfpI8/ZTngRHUGiDB/DSj05FIkEOSeQRgr8HQcuTJjVtCcAPTmPCaH6qAxBsjLIHpnhxLwxSiR11o6d1gtWesSIMLLwsNeVkGmv/xQznMVJU2RgD4/TF3SfcM5Q8XYx2M9p2wvrmw1PZGdIAFKGRyaKhv2gdKg5wkq3GupwzOBgcXaFBXWC25LgFevrQrZNfNkzTiCm1BkY4orkWSflmuIry0p8wJoJdWIlE6OIPmIahbjSwmyP3MzJ7HwxeIx8wEXTWJfKXITTyIMm2UYaXlXIqNP87klLzcu11BuCuySKyQ6ybLnLTwlLB2odrlMmqyQ25QZp6TveUSiOt7+96JTJEWeSdjvEmBVenZbdnRYJmnc9HgvJu8DFdZCkQ6mkFStaF84+hYnSQa36tFbISDZFyETh2Ognop7SL/RjXitx/gKgT6rLGSqFTPLGifiOfrTrW0eYjHIrkisrhkyRc27KrNa/oHA5NKR1M0buOj3ZxoKe5KGSm+LodH/VXHwxsTeCrJTxf5TbUnkqfw2Tpv6iiWO7u053zD00oGJdkvHrE4QtxosFR941uE2VDBn5m1MZ7zSG7tYFZY55EPpxv066WC+opAvtMuRVT3txXiZy9pSBptX1gVbPreUd+WOUvxVAFondlz/BgNpNb4x0fjKj3g5Bw18dDHb+s6lSMQ5akLVv4KZOcIZRTIWqDO+I3kfMfzukSG7BAps26CWNxUzUrsnFb1Om6Dg9HjMQ/WWyoQfBPgjJopt+vEdI83Wo6gUT1EEnIvq8TLT6Jz9Z2CYip6MpYYEfoL9dC2R116VZnLTj+o4RtZpGrQQkJ3mWtEy4TVbi8LRcFQODcqG18+CkaIwTD1KUcIytZ4aQ4W3QskC8Ji1PRo4MpXGkkKxAFsvexjzBbUkylDl/OPmWvp36aPlsvmmE0IaiOM1fYUFmQ6tBEB5vcwuqSliuwcZMxn0P+iqx8AWsAJKEn3dPLCte8QPVCTQUTMPVH07seoLKHfNsVqpOugPkqpDeec1rKveK2HYxYWxDKJHqz6Xv0WTqj07MtC3180atTntvTnO3pTnMIv+LvwV22k6jxkEHVUhL6SNCUiduCa3TEE/VH6ynz3f6RFIvTFtH1sjIg87lJ7BcEoTzIlFYcc7OMOFmkKwUzpPPMulWm+Ed4gfit7SsErPZfq17p8r5xLWDlKZCxIwQigSi+nGDlJoTC+IGIJjPbshTxhGrkDdP8dVBb04TyHe6yaecRp4cSEL6rHojZsZKroVvsqSeetAFFuEzBkrsq1FTx0lEyzqpv0YxojdUlTOPttFfOtFgpBN4LNyMkV6k64U8Hg4bWIe8GX3qk0nRuVa4EQxhJo0OLy1RoqIxHKbvcrdgzrlyYezfd3n6843jbg/0Dx//SF3ZeuKIG6LLywHmdG9FVnN2gJQeMRmw4IW0S8O11EW5vTn9FC90KCjprwGla5SFAszpfV8Nny5V2RRrUn88StfRZI8aQRSXqlZwmzmDFWf2hWQiHa27jX0CPOFOveKBnZVmcdKO6xkLH5CKkWCYIX8NWiPQVLKCk+ggITIEjmqTnmjCSU7t47V2x9o5yGXCQUwJK4ewuftZZ03QEMVRap3ncBxI1uBJNl2cNd0E+Y7/WW2NIuNcDuCObT7AUGgWOZu/WoDTNDbbNE4zjX+t0xAqgPU11dNSxrh7YldsfsFYhqpOlh2V2+WBh+10n/v6qII/LLqpfJ2syttuZro8IVEx/AeiYXK569PC4dS/No223/4koaIObQkH0kiGoyCpZeZkieWuz6TlT5YclADb9Ur2ZD3nuY271xcIG8YVNwME6+VMZts4o5eJDRnjMSJF4EKd9CxvTWciFfxVFfjY+ZFef1VJcCEGQnDeXxLrrahucvBIkHukQ+aYEDYspwFiuH8CL+abPV3AiFbf+JiNCMQ1qONMpQxzVQI1mEml6EfD00AzdqK2/uZqA0kUi2QykcA1R6OcZQKdGGLslmZKZoqlwu1QggyjCDWpMCR+inVG0BwCBoCsdCM/tZxfqTD/X7SL1bSkRRt4NRChmjQ9/gl9z3n20Af+3+Gt20YwRTFOhh/3qC3PfMacsOXd5Aw0t+OAVgfOA9+RdrVsWjeltEuTe00CyFivESo3GQt9RCApRqwDU3QlFUfRGW6GOR5qI6RokU76mSYdR62JOaMJST7O0H78ytRvmUBwYpb1g4p50M1uJj/lfHa261mctON6BMRmAFiLTESfSAX0zjddkC1RMnQ3pck+wQ0ChK1buc/PCbPD6eHGjDy9DMQAM8ymSLHcRtFPWiOomHEOQ9iUoaVi/QwVFfPX0ALywAjxUitvGLr84g3rVg0MpfmmZx7RWEqB7jaH5pAwILskz3pjruhLmAwTKqQO9ZuVNPmrvmBF6qs5uAGGMloQXnbF0VMtlSYI2PJ+LiVfsNaC5YU0Ab5MF/I61yISaYwsa2xNc/ztHLg9lrzw4nA/1EEhjWYiiKXELzo3P1K6oGu3Fx33WR2T6b97RZ+ntIuwtcR/+jrvwaElOd4J9ghVMpxYnTMPPKMERj690sknF8tVVvcqRr2Sj16G6jSH5JRJr7EB3dvXvM+/nAl3lJFr2I7RKAfseHNE8lg/duIDuWK2eYAQCYFCNApUsn4BlxhfFIImGFD+4rMLqgC4BKGMXwx04asu0+8Mql5jqKgOa4J8geZa+tRwc9+D/noLbEuOEXUAi7UORpXTuPYTTKwUng+LjyppKd8i0VjXjzFg/H+/Rj4ubJg1Sag7Zcc/0enbsn0aL/7vjWMe0XjvuzZfdllzn3ulN/6fuUc9aODY43qwaw7QdyhaS0OOt4aMUFFTPkoYe6edylylrOKr3oGNjALz5zxpNA8hnzpU9AJ5AODIqkmRiQpPfPdyO7kmyJ9m9u4Euh1qVDdr6/AxmSwTkSA26q8huGdMYaybLfVG+cGd/LoHedrJW7LzV48NJtZmzMgkz6ggQ+FEicrTHSbc/B/uWxeA3/mHDQ9CSdQiFI7klYeWB4kjbTUtZG4nc2rRqDBQJ0FACeULaljAUjEeDnm6QP76XHX50KfefP3yP3zvOb/50zPSwK/S0V9Mp9gHbZ7ExuCA9NYvYndJroDpFOVO71sWLJMMzais6JmCWxSn7yoENFAiXRIjZC1g5FCW6hOWDLimdJllJj0CjkRtq+lSse6WVVv918odvSy8nQwAlHYXjhz/HUZ01L5aXak80uErW0iu6qTa5IqQ+LWlPag/MnJneF7ftuHmHPIQJtNuAGuyrtlaRrolkc8xiFMBSZK70wkiSMHMe1ksPTsJYqf5WYlfBQDlrEj14GBh7YhlZRDlNCRwKoTJcEysPI7l0+xLW5cVUjNfQPOEHNBZmBA4SQ7EG1qCQgwB0yG8xWEf6MRChgsm3E/mGLFSg3Vuah1h8KFDROS4IwHlIyv8yZlLxtQ5AYT41clTwJBMsIvYGGvBNMXJBBmPvB3xbi+zCHvc7FU0cjtQme14BSepgPE7aXrQUf0PMrN64MAdd6b5c5vHP0YgXesVt3ZFWK0Weq/ydYc0ECkkEZ5zgXUqnCWT/LcTs3YFTXgfc/A2PuSQ2ofSgvzkoxqcdta49YgKuOMakGUaej2DasLK1BMoXS9bmzbdJRXkTbmkokwWUgVRcVISY2mYA+4uE2NKY5emHrTwLt3OnaHypEoMhJFmpYznnNgRKQHCnCjIwBBdKJrHRMEAoKFMTK3L5MAjCUxhaXmYiVfJEDKiDF0SKVS5nd3qqeExMiEk2U6OzFyR+gvUDbIKY4KrqsX9obRk5fWHnP++I4cvsclGpOkj0w8PS1e8Jf3ztm0DbDC2GFesgrAAQaBmukaaxQLqntMl5zmadSqWgbwsVrVNvDiBzePL3iMjGYe7n8roEdsAcdhh5dleUgQGOROL56K8tYWqYsu1tA472AzOrZ76QqHWtugX2GsEJ0Xjxquf3OCOF8RA6HSd8IeWRxpV0WvjE1cK7RtqzLnp5j6dokMjzqyw9vMLVos/qywWy0qylddvvfDz67xWnJ4fn3l8mwAqGR3ZFLV6vO097QJkhaO1s14SGFOIJ0k9Z45AUugBMwbSRpuW1iTvIB0Wp7FPWnVVOqRZsKUTE4zukce3V5DsYbWtGEwUpyD9AIqhGP5AOUBDGkm+lOy6DgKetNEkwVegFkE27FaF8IR6sXRSx0l+go14DjwV1jozTbkKIgmDg8lroUO0yBifN1Ex0+ieQBMYhQtwivTwR9Ta1nWOOMNVbzZlYdppTGWym9XAtCBrFkcr4jC21Ak3ApvWf+rmjjyDty5oA5zEYVqdJFs6wHzzN3/pZKCZP0ll3Ine9JcX2SbNHYCTtIVmI5wV+Sy3kapks6wMCv4jDLgHB0Oiof/LKXet68mL167Vzp2htmTFlE4IKi1mymg6SPGxtTH2BCJwWhgGAb2NSd4RAkfsjGQISew2l4n75pRGkcmcDA0wszFOjpYDuNdWG6IGqqzkbORPgrIyzc/Kh0qFBdV8YOvGY7b89Mh08fq0yAZ4BkOE0tPSl7+VTv55ehRNFOiE3jHltZWMP99NTma+8bDWOVt8s6WO8cgrPliniDKBaD25yjWc0rclm48Ht+lHfFAZf5EL+oiV0ndsObWVsVQUCtc60YB3uieSic61PxxK7p6iu8vcBuCoJCizTlx0VBUDHOtKXYPgxrbhRkiCADg4lbLGdm2HFxUt3Ljidb/lpqHnn7r2P69bCo/VqzGjd8itV1LweLUjFVZzolhmWrvEU7kv/5F4lyYf0tK0xvnqzom8My3dmPZvFuuqExB0YmhXSHezkdbgHMdMYKzSmkhQI4YV/cDGc0J7CkY3x8AKowYZzmF0K7mVv5q8mYSQPYJdJq0IOVFcDKsYBYG3FOFBUWjlGRrx6H5OEmUpggvwHidcgiYpE/4Sh1MA3K7pH2NKEBXGwtZapxey8JTheL45uYbE4zIHegARf90JtuOPos3Q1ERogZVlYE4X0EVBrkFkyGknIa3guTR35VbXBNXDt1zbnaSSk6jGiAWYxHMz8Mi6dete+tKXOnK+b4Tud7/7XT6y43bDhg0vetGL9t133yOPPPIjH/nI+l5WDmagmlPKksSYClhNI0ZMFwMW2kem/NXUq1+l8aGJjBk3zR2NE8tYnCUSRwIzY/ZYwIElKhGR0zWZUKiclUv/0h3TRfQs+2T8P+zwjUek39I2jlK0Z9l2vQ1p0YK04bh0sbI0sxzzYYDRDpzefDx8yxVlRE9RGSyEbBHtxodfIdYCVOqJjHNQwHIYlmKFr7mDAD6GVndojtJpgQo5SkQ16t77SrLyV3pQ9IMTmHiwuq+qitnACCs7iYA7QSVZR3bO2TAtSQLCZj2RCquwsWX6fJMMWEJiJ5wapzuIK8n0lxDyQIyMzpyWWW2+541rLr32XsNNBc40ccmUPwU3LAeMLfm1Lb2n5o/lEgWNfev2f0NsGYQkuekMr9KmZE10YsZYWf7kJ1t0E57qDgPTPAqysUprys6v6eBTo0DgiE2v7hMkCUgFZ2qcwda5vsSbz8mLAk3hFFH+hM3Oj5P/0Az+unYeBG9BWUtQVi3dmTmH+oXiyNVMkI6mNEx1DC6w3mjt3PV+1RaNiv0ZZrMGC+bQydQIsTdUcw5ZJ0TN3c8/dd8WbQ+Sm6LlgKtBNIziTKTVR6y9rtGnpnDAHJVI+eSULuTj5s5D6hNyos40FdbtBrQT4STcnD9//qtf/epbR+jSSy89/PDDh4eHgaebbrrpG9/4xllnnfXZz37WxVDdRu36XcEalQcbaTN+jBYfQbic+YYNEw6hm7QZV3AS7wXPh/uVcTsVZuRAqJiSTiWr/Kw6n3VWsY72b+cyHsWUGlSKX11QzltG1mh4jEClMpkqmdhlpVD+CVvK96dXIrSdVuLMi3AQ/aI4yotOBxFcl3uqXCtR6qBS99M76xricxmY7rlNMKyVgC8q491YHGb4wMFANjoQBMzpyAYwG5ogE58JyfTaUQ4ariN0Yn6EvAG+ZrpBuAHkCdj6108UUefxmTDz7hMot1z6CEIqP02WWJgmb9m0kriizXxwD03/eUS6cl7ysvsxO9ZVMQ2h0l4w6Cmnq5sSlB7qxHq9CYB2cIxFPnhCCeQecVN32Ez6trcV8omMCFHb5hjtZDXy8Zcls0LHO2UaoK9hCxQqxd9s51zISgJ2Gg4TdMLFlcnghdV6ct96lu4y05t2Mppgo5NOKqpkGmk/LG1pudxcwjwHntZSn8wo9/mQvETIXLTsltYXnMfGC4CSRw2eY0VWuZWv3bcl9wtU4YN0mXGEk4abzhWMryCspovofGEJ7tudx4nrQht9dDoU5eYszSgHdq51t2azyau0cuXKefPmHXjggXxLq1atOv/88y+66KLjjjvuqKOOuuaaa37xi1+sWLFiv/ELDBk5bTOrzQI4o5yb7sz5MAwAhtnYMPaMAfqIHjFXgB58zTrREDWuGGODCmafXuJ44EphcuiRXknF8L7OfktjFs7Y9ZXXLrj70GO2Xf1D+5SZECBpUdpwR1r201QMdBpBr9JNWQeJB2SP2xFlQXkFZea0S9z9fbqeuvzgB0f3OesFPQKFDA4WFXNNkdWIBR01fp/+dIO5ote6qRKtDST1xGpNZqIEZlUI2zHEbimQSOmhectp1Ie651ViFSxR+eqD2+0IGrPuCZBR0NwYWh2GWUEMjM7KskcUQfYPfKDRbHbY5B1emTJcaFdy9b7lJMwXv8XCtaRGGvJGvNwFLdP0enNhWvfAdCVsvCktIKgiQ25N+zn6Z6SgrjIToXVHccTDsKOwCqdSd0TGyHwHsW+XDd8SL2OZ9BRwwEdrfX9CUi5PIYrRF4LhOg9nve8mWy6ZJWZBdcBrJgpK2GUHImxBOcODDy6WEctHGXV4vKefxPkBPf4qlL9ZnTGBZkPGheVp8V5ucr9xxJqrOOk3e3OlhxQFkmqv5tNIBpQPgfcTUacQNCEPHNeyQsrKTSu+j1C0un7fj5khruXmK35ywMRXI46qyZTzsecDNhKSYaS7yZVoPS7HsOb0sxfTy4GdCCdZbYN+uJHe9773WWs79thjzzjjjMsuuwwGOmYkCGXhwoX3v//9r7zyyjVr1pRxkpW4Sy655Mb/y96dwFlWVffi31Vd1dXd9ETTTM1UyDyJigEUwVYJKCgOcSYaNcYhk4mJSV6Mikk+UaPvJSY8/3mRCHloIjhGfQYVI04Eo6IiGuZumaEbmp6H6qrz/567qk6fOvfcW7eqeqjWuz63q889d589rL33Wr+91tr7WAjkR9Vtfbjz5fzO5eX0chMCbAEkEJUSMj/JHZOZPAr0YHqbjTFzTCq/wk8h16ZXbPVp4k/cDyMH7RiyoHaSVx9rTHtISMVQ/I00wJ9jbUOk7r9szrP/6vSf/Na5R6/+xsK0lhBel5Z8MV3045QH9VjRklDCrQjQoFjwjX5p+q8sRwwQIECMV5nIEVWaFNHEzhewKC9IHXCAU0N3wE82BzU5fmENkpJUAwVog+F77pl10kk1r+Es8iwu5ExAt29mkZipz8pSjIIY6vHLhJzhNkPxjhGgoiwBKYEj4TgoHo8L4IZ7jlWA14bEJ/oFmkDnFLMxRky7xjSR2s5J4h1GzAaUmT2M0uC5UeEj+BfDkcpfccW4U6Dyu1Uq4NiYtqwmaPldifw7hgSbWQMnjVoiSw/0dI5dSk+1uwS8HpdWbkwLHFAwN61tOIjnHpQeVrYD7ePJUF2mKm60mCO9DV9bjIqiuIk5YMlksHVCBo8xrzepVfi+uRrs0IyOQH/Z9tMmZzpYwBCe61YCp2iaxsINyJgxPEy3SjCvoptLl96IMlw9zkIGtLGaG6uB9YGkeF1Mm/pM7SdNcHaoQn0qFVN/Rz25TyLxNtpULzarvFDRcHFgYaTBB4YoIlFgopmCPIgPOOOrvyHoouGBKSVAcce1Ty1b8kRjkJQYxxazz4QNRAsD0Wamm0qqgympi40K6x+IjXmMAUkb/QohcbB2aVdzYAbhJDakF7/4xS95yUt433784x9/8IMfHBwcZFKaPXv2XKOjQa5ZjFCZL0NDQ3ffffdPxf3nGnpoLw1gosBC//HdMOfQiPAKeWSmmajmg91epqiv5gyQZCVRLOjL3JjmNYFLfpmT5KAtTqEU20x1xakSgueQBy3FrrsuV9so1C3Vbraj2QPp+OXL1r//9Wu+furdX/jZQ6v7f5SO+1p6SqHnqHmSq8BJYKL6lKVYnkuDwMqyp4nAtVeWShCUUNDZZ+dLxkmRxaUXj1QIE8gv2+6UqBfYY0oUlqSYR6ECR4aHGQ/iupSw7pJkhAvbOFlwQIyCfsdAupN90Qq4ud/ZEoSdNsKH82KEXXNnQHuVOOuowrOelcd0c7UAfHQeOUtMU5AmGa3mQqAVmyIx7RRyOdAHLJq+Et+Dg/nIBJKgKAALYTvsSJS3oYayaCdqDKFWY4wWAaDFoumF1jSKRdrk0/rZml/mpQ18wbPT1jkpP2QJFpqbH/g5vE/avD45MSY3DkWFaUe9Q1+Gyoy8VIPa02WbNhkGHY2EohI475iumE3FzdoLGIgvm5qHlgyMVgzUm/aUCbrnx5mQ7DNnxDWLqWFyJvLUFrgcwtAFRghtrbjKCiT44JEKMSSL9XHfHhRHosjWMAN8mXDks4vIiPVR+VZjxk8qjBhWpayQX1nmSL9YRYBNZhy3sgV4aB59jSc+UkYpcRFQCXzBqyD3CzIqDAlpPB7pY5zIJ9ITL0p0U3GEnunpWnp/iTtSNBbG7piGPl3abRxoJ7x2WyWioL6+vuPB+AYdeuih11133fe+972nPvWp27Zt27JlC/DkFzBIslmhdSNpIsoXvfzlLxfJ5AZDlAfHftnL/qc1KS1TyNTlQ+GRIdpADbhBgCRTkwWZn7hCbMG1xB/PhnaNFeVqgUVImYqwF90cy6PaZ8homAAyoyMJCw6XmP+RuHbmS0nqESIwlggYj0dENqEPYZRF6tIDep/zmmUP/PLz/t/mrR+7apZY17IiUc+ytQYysKfG0SaF3Ik6WOSJVyU+yiQcVc15ZywTMSfM0RWrPnUOE5D1ZFMtGUTNslWT+ZVsHoYw8BBOKqOxpnz6r7pqhEVnDNs3/V66oWfFaTLt1BJ1Qs3o6zIDa1Paz1yAJAlUmN+Bo0F4SoVL8bicfZrJ6LIXmunIHkO6Tb8bkJjPVuEnWeEbltIcxSoWfjJWx0iXlDTD6F32ngnkTHlQjWWV/684ox1OKnVKTRENlJWPBunVtlVu5ZzbXw+MwaMi2RiQw4Nx2RvzpqFPGSepBhBp0U8UtfIV7si5MYwjU9s8nYXRCbjHc6HctknGg6y/4xePRfa5xvVOSejWBGHIMZZqh0Q8YJHmV8PSjAa/PGv6Q0VmIsbG7LB0MYMqRIULJTRNykQmxAsQBwbydZ1pa+KoAOZ0IrsUJz2AYgAYgfAH9N9JzLKpB2pA8G3IeNZNRm8twU/xqiKFwijWFQKexCp5KniCGxpiFpTNeBqlF1Q7Rkl0TcHtQDwyjJ7CE9cociCZgdEiMV3gyBi9TB7qDnO5E+dpbVu6N6fPgQnk1/QLmFoOYoxAov7+/hNPPJE/7qabbhKftHnzZs61JUuWAEblbNmcgKe4w0lXQVHllDP8mkSg463dzUDq3M4XKxgTngmBlGE5N1FNQmhAiHGzqGpunWlmwzAp46+Px2MxR9wsX56vg9sQkc08A1QR3OALEeBx0iEopIzJDHmQpyqsT4ADL9l2LXqmOYCmXNbBh/QuO2ruCCPT+MUcUTLWk6PJRWJa3ToxweqZLCazaBE4r3JevtQqwE9kDyBTh4qNQe7RfGh9C2soimQk9dhpWOkIyg5JdyCc10ArY5vkx6ih5ca+xP//8i+ZN2Z3gpO0CIeh4UoAO3HJCfuWt+T1xM/2RBw3nyzlpkP/wNZOFFKRv11sfDRwksejrxve7NHf3QQTWY947gqchNVGYyiGRroahhT5T+rCSKCe6Q+aqQ0JslKk43+lMTsMIYNzmmQb/7L0IA8k3NWwO6TGqyNAQDyIG6Ml+E7tVTAK1lFv7W1s5RpGnQMHMCb5OiERDqLysR2XTHMVUJNWxD5twyxjLWaa9aaGMzlrB0bgBnnS2dBMsWjxFIGjCEodbKo43ZQrZzvv1McCxlMuHF0GvhseBUnTofvPI8oC/VnCQDco0ASUJ/+X9dtf/3WRZf0FQMYszR3chuTmo6VlgFubHqPMZTJQ4sHBnNVmQYQHuaPX9BcA6n4MVDWXXrZFzrCdx40Kd2KoYAvkquMCRuMt9OlBA1gyvWA14oDQyMdPWNelPciBGYST+Ms+8YlPnHHGGZxrX/va17797W+/613vGhwc/JVf+ZX3ve9973jHO1auXOn+xRdfvLQQ0nuQc7ugaIt4M5CEChFGznIDCcsgGkwY88qnc7IUE+4jpAZC4kwxCYN4eXxoepiDpGhFfqL/YA7zVuJYCNJAZizpQCKY22SEr9xbEBIwR4iEL6ZVnuX7BAEtWHE58THBDWUCE8kLMpegLNgS0EFlsMtGFdHQshLhSIJbwsIWfrI/X501AdO0RVCCl0mBdLARaYUnlD0sUqstyhWIa7GfQQSc7sCBkHfNKSd7RyART+Xf/d0OqSpzh/XRZPDcwMDE+RknReeWU4M7FsGiGTono4W1zGBrA02gee8wLnQVDcFfbAt6K1iA+T4UwGRJV5rodLzHxzfQqB0bzaO/QBb5UKZRPCJkZ3z6yZYs99nr0sJF6bE1aTGvmshAEUscII8lZ8JW50zzSFA6vUgFmjvtSdMQ5uhohliB852AJI9YNugjgzkUcPtS/BqzPpKZPjoRjNDRFTKJhD+bIKa2qWEe4acBCcf7ql3mu/YCQ9YMhi6bWUxGDTFcRSIaDBL4qi+a4VSluDZfzXeDisHMAAhSOp+gJss2zFStHicHNERiqK4VaaDWBf9bpSnuYzUOhLhzU2Sea+tGgEZ/EXqDg3larCNpCUZIVM5EkK9EkwrrYjNLJhgoARNRjFVVtRRhVLYuNftcy9kikyWYqMH5Ls0EDsygfmBA+tznPgcSMSYddthhf/AHf7B8+XJBS5deeulb3/rWl770pQsWLHj1q1994YUX7r0WI13OGgwP0dMWGfQ0u0ioalLALDIxCs0dQlB6GqhZorUfPaalkxLtifW4nMvkq/nM9GI9GqG45V8r17Qg8y/sEjGYvgIfCPiworJw5BbhGSQp1LwQ8QQlJW17M4lABBCpZXlEPIkTv+SSPA2yovNmqnVp/pa0D2dQxbxMEkls9SxzFiNLTFySmyXj8uWJrCdZtMgdNif1VBPOPtFRdt+onqJBHBiOzCV5/SqlCshEL6h8BXqSTcLCJC6THuH7K8gbqeBXqKvBWMytKs4PvH9W5/2lUU62pHIcKMBShZmUkGU31nVIzE6tyIKeRiR/sRHsiJ0+2E638cJobIXgSCMHq9soeJ2r+QXhJ45Z9zvwsEJ+wjq5ydNnskQP0Rm6TB+VzVqNfAqe5xf6ADE0CswyPo2N6dPd6dCDU9+R6S671Ww4cDLq7enILWl+hzlrOM1HvzbCJusfwh9oWF9jjq4hECjRDsmwMeP0RcBTWcU0Ly7a5OMRx1TqYqVz6Oiggnir2VyZJ2WO5KZbrVIcTcKoY0yaiSKiAgnhtrkA08dol9iqxmenEFuLggqQVOSp2qZee5yE+eQb+zGY1YoYmLXC0JIGqKpQgL/iJhQbADFmh/YycruDe+6QjdruEZiSzLE2c0HIGLpqa90bcUXusxwDQP/yL/l9jxjeICk5bzJyrzvHAcQ3T8lAg0ErujRTOACU/DyR+CQWqRUrVszMRt1xR/b612cnnpidfHL+96lPzS6/nIdxtLJ/9VfZE5+YXXhh9tzn5p/zzsvOPDO77LJs06ZJt+aKK7Jly6iPdp9nPGPS2TY/MDKSOYrhoYeye+7JG+LrunXZhz6UnXHGaDM15zvfyYaHdzz6zW9mp5/uLELn02x/YfrMZ9NF30lP/lI6983p/5x0wCNXXTWaUlYa/sY35vmcckp2zDHZggV5o845J/8MDmYLF+Zf8fD88/MEfl2yJE+2dGl+cdZZ2XOekz35yXkan0WL8nykDN4+/enZS16S3XjjjloVVzfdlJ17bl69+MyZU/wy7uKxx7LnPU8aDjkQYMenp2folltGxiXdBV8wZ82a7AtfyN797kwN86qm7UvSI/unh4Ccot97HWbUYgzMmpX9xm/sGHtRxyuvzA44INt335ZPRW54/rWv5RVQjYJuuSU79thRpkmmSv392ezZ+UWrOkx4v68v+93fzf75n/M2tkmsoGc/Ox+BOkUvt0m5237C+Ve8Ivtf/yvThFaFHndc9tWvFvyb3MUjj2TG8MBAFl1cMLm4aFVo+b7EF1yQ/eAH44pevTr7h3/ILr44e9Obss9+NmdpQe95TzZvXj7v9t8//7g20fT7rqD77suHU7m2xbXJ3gn9wR/UPK7JBMVhh+WC4nWvy+6+Oxcyr33tuJTSVBplnF99dfaUp+Q8J9B8SLAXvSj7yU+y22/P/uiPcnGnVmefncuExz8+07M+T3hCLs9POin/S+BL8D//Z7Z2bfaRj+SPG6h+clM9t23rpEHdNDuTA+vWrWOFcQ5RJ5nOIHvSTEGOu6we1hBxqCAzrJWE9Z/lJtOF5W9E2wgTZgixILYm8ysbwOBgboPt3DgRdbcQF7XN3tOerBGnSdZMVpmWR6rNWsPCbPXppigriySVt9hiubHhxaKNaQdZP1kzMZykbOQV6RO/lf6+P23xXhNvwnpd+vCSh9ff+cPXP3beIks9MbDMRRpiHWZ1FXYsq0wrNh92Mn8tCpWLP3gbZPWm+civVoGiUJm+rcyU6xUErlmbuCoYTjwljdpW1m1O64l3FY/m2OI/C3qG/a1b5/zys4buf9DSLzds2Dd95ZV9xx0X1y2enPZtXLXiF0gkAJwx31Cxh2ZRWrdP2sCyJrR0bZrvdRm2ZYWNobZADReyzTfEOVvYAKxrbRcQERK2itoH3dQpjDeGsRAo7yiI8ckdyYKIsfJk2xPSi89tKtAq8/J9ldS/LJpMX3/6p7kVtplMJeOEIcQCXZpb/jvjL1uaHrs3Ld264wjsks2kOYtdcEcf2YwmNNDQrT2ohDWCo42PeGokW8ZCxgnzCxV8Li46yVZiQUv8ZfqRAyiIFcRbpX0qJDErlHaZ2vrFV5w3g0z2972vknYnfDVh+dNriT3MsDf82pPAStUjckMgsNMwMn3wgzW+bKZ3P2GCnJnY+ZTLhnaNJY70FOMWExfJg1gB7WZgVVJPZjlyj2jCQwZvE5N8Y1JiHyJMyDFhf/pLERwIMWGZjQk0nCfzTSWc7NJM5kAXJ+2+3jHNQAS268KmTcGYw6BG4CRWXBHWYkoICMpb/JDds/5OlsLnRaK1pwkTND9OOBIHVJcm8MGpJ11LEGiUtmgdWRPOeHIBUV10sPsgBcOymyRRbK/dP606P/2HfbUPpoNp9w1p/oK04bR0w003P/2mm57MJ8gvpiF0LckSIAkqUmdOQ9VQAZkjIkyGfAdkjfvAJcmFexCSBAIUXLsISeQmXSsZ2SdnMpSo4h8pux7yTDsmUOwb3yoLuQ7iiTrOvFVCrcBPW7hD5uaQM/U+lhavSwsEAs1O2/dN67R+KM3NmJlaEz5AWjZD8dwFB3QiXy21R9BzrjV5u8blpXTxKHRDEeYlE0rU8OA4ELUGbuJzHakWa+I2W45ij31dmvyeGup0PcgvCX792Z/VvBaGQjKuFK22EN6S7OH/kd7/+HTjV9Ozv52eYWM1nf5f6eSh/Gjs3UqwO6dnK7IzQzSepk2ZcB4OmD45c8FezgInNWeoF4gpgAyHTTeLjcBJkdJKD7xGpqTZZFUTE7M5n0ndUZCBVEvEAt+0yPQJyXYKY5tfG9k6iue1ZNA6WKT5MCfShuQxEZRoXWScE9GEnpYKJ9BMb1WyYiFbePaFHFk/mDuqTR5aA4PvFg8GMFGDvUoh08wv8xfpeqJSpEHFx1dbw+7NPcuBLk7a3fyvCBHyPe7Q7uaknVyWKeYYFDI4mAudUGCTqiVtZ7VKurWnCRNUHif3BVuwQlkzqR6RAYWAHSQIuYD8ZbPhrXdBghMfRAazFiFLqME3NqHYZcbkk4vU4YcYiTakfWKnEky1PfUtSo/sO7Sa8oa9NJwEwQqCRm5q6+Mm4eWDXb6qko8LPIw7wKj0Qe6Ttj4ulCgl+e6jeoxblCuo4a9TDMQNULeVrhnNZcb8p9XWssQ03GmFWoo8y3vaP++d9KqNxmVanNavSkDbBHoYey2R9QszW5AhRwmxDBHo1Ex7MgCAISg/jIVAqt3UDupcuTJ/rpafwsPsnhZMf1B6cCT1P5gWb8lPJKonukQHBVkzGE4OqhBuVZAiWLAMOQt0QAqIf2768vL0H2vSvk9N3zwrfWPftGZb6n9b+vvv52eZtkONRZ6TulABowv1pc1HpAeWpdUOCvhZOvCBxO3dzoilH02W9ttC29fEO4u0ffpEi5sabcisd8wEQyMBZRBWyK/x3gw4QBA3XMKIMh38F/ljrAwtq5rJT7Hsaf6p+Y6QagbvqRHZ5a2RxpuBp0UhnG1tUTqZZpzb+4kIHCFTxCAcGZAo5KH+dTwKy5OQ7RtuyCVnsAWaJMfINLCJRYqdiaDziHZ1aWZyoIuTptgvtK+pQk+wKPA3dbImYPg10ywxzRbpSRxThVoyVagrEwk4sGjzEwjSHGLceUWtbNiNmWTaE2AxKaKN6E5Lq9ANxAdbEbkQOlJWWkTg0t/YokXgGr1FdyKSRXyoTTfe6WEJRbDe842FYmMPTI94NURUAwTanOY/snU+VljDkZIykWEUF9pIESFN4mv8jcddq4xPEN66ltgHS+XmWb1GQnHihKhiESHgrAjZP8RyWt+DUzOWoDpeEgiJdqSxipaW1b8TO71bYluaPS9tJnUbGnyCBukXUKnASVJjlF2EigvOt3+eTY6PxhgAQD/zmXzV3nZc5UFp89IWLkJHOC5Kq+anNSvSEQLua0sRZsvUEaQHnSJmyAkljiJ0pZvIZkbay1Ty9cx0vTubco+b5vesTYsAsuPTzd9PJ/X1ebNK3ijDYFLkCYPTIczN1q8A7g6XPy/7+nnpmkPTXWx7t6WTP5decF06sw1UMsKtMTo8Z6u2tnDhhKSxoZvLFqDKU+Zmm2HPeuSgJo5UOcitDZmqLCiwAofXdPBfFCH22RkfkEQzGQNCy3cDMZUZ3taERLchZ33C+EpWGGmCxAlA2IhsoQjMR8AX4aQLQg9XCfOQV1atekGPg30sT9EjGmgiQ5/6kaiE5zDNGrJLM5ADM1gtzEBujVXJ+KZdTBUeH6KZDZbNdkJ/udnCfWCzqAMkw0BimsVSHjKwdrd8Mc1MLVhKztaLJMIUSLYFdpnC47WPaDJtGvZnIkAlCVDGG3PeSsgdpM5khDlPeYBKYciBI4kV0ISMYFKyMKUbHBLzb+sP/v4Pz3hBdueStGZTflDNkLNqbpn91BsePuquTfkCzie4JOcKHqqtYbNS95RPSCWVIeiRmsuZbAoPIDGnR7TLSz9UlVlij5AqMdpTnFrB0wrGEb64CkPDCppgzIhHAelcaxSQFM1pqi03kyTbGVGafqq/Qb3pRxXQcUEsBI737EQNS2/QBuzAWKcYB4IJhVrutbGy83sL07p906NG+vo0n/XlmLTih3U4STPe+c6qRlRbM05VFWGY+VjNW7jrTcrMimXklmYrjkhyh6zlCk9VDarOaVbatjxdf1r6qTOW70pHfDOdfm86uIyW9EJf7/DT0w9elV3hEIMtaUD/nJG+cWB66N504O3pqFZlYYRRZ8qo8xQINAzFPOGzWIT0Tl135D9xJw0O5he1ZMjZOqqZZkeseWqTFTehB4YTMZe6bzpE9DFNWSk5M6lSc6FCYcGaWv5aEQiGxI6daK3ysdTUCtYg5EI1xEjoNdMTT7CU6DMTScJIgEuSxYSKC6MOcFSiUaotZple44OjC4xYD/rKDaePZEWJMMgxbHdppnGgi5Mm3SNELZVmI3QYk8wWsIZSMavbzzolWYvYScskYG1hYnBYECgmjOU7iQnfxMKOGcadsHBMun6NBygSEzVUqblaETRTyFP1CAjTOyCRie3atJe5n3wNieCCzCVSWZVoX03j0iIXiAm+GBKBm4bbjm548cUDD5/yoi/969BZQ9fMT+uc6ffjhefeePRLV6w5QLXlI3O8RZ1UvpKm3GSZhOnFTR+Zq7BPkAUcTOk+25LIMJY8fNvNRFyK/RQky0qvJoyOBDFpq6pYx98ETIu2dpKWMWOERGMNklKrR1f6AIGzbxyyAH+0MWZUGijiWCAFy40RiETmdgiSyvngMy2CNAGV6pZ/bVCEJYkw99K03DlqW/TatDBOJyreXTOaNuURTtRG5BY35W9FYSy5aa75qxQf19jiK4/Pd7969hnb/5O9amOaL1prca6MFv00PX7xwh78NHljUBWltLkwWN6QPnpR+jR3MDum0PhT09n/mF4LMJWfmjey7onpu0vTg0LE+I79ZMP+UenWp6Xr2+AkycLMWc6q82s4yRRrT0ayxuKPv0WrMSpwbTzLDy4m0pBrRfA6Be+pYtHSKmXcVxzBqI/Ih+kQNCzG2USAuhhdiFx5whDAhHewwMRTIA0XWS+2j/h1ze7O54UDzUT4WJnwPsP9Jp0PiYSHhJi9Amal+hhYxCx5qGIoxiQumblSYr47nsI9OClUgzukonEoWN7jrFDWkCadZGSUaCcdIQygSzONA12cNOkesYBmSSILjG+TgVzwl8vMtoUJrcFSmgksBACEuWHyuEM7ypNcIIyCTD+zy0JkymS9xXNnLYjUsJYmFSGubj6FtI1s1dnHbDfnTX7ygjOeCNO0kCwgkdUY2ORYPMmICdLTr5qMgRs3LFt3+Gu/P3zhnDWrFhyxcPiQg7/83X1XrcmPx1JQWZrX1r/NzXKT1TByU7omEHwEWZCCVBur1VBfcAsSc6wOu5kipqeAJkS5j9oGsVyyNRLZIXzd9BMVi9tjDZG06OP8scZmNxf5NW7jRpkhkW35r4Z7GRzXG0+HzNmTOic9jsPIg3hI3CvLtU9toVhO4xT56xDWxKL2cd+zNKLwWxMhSFbUm31JTG6hluKvXyU21I0WHy+te+enzr125c3PzK5ZkvJlvq2UV6Vf23jQkRee2wMHS9w5nZRuuTh9RF0577TJO3HPStfemo6/Ox2wPd9OOEpsCvumVTy6sGncym1MafiQ9MBYkpr/MY2rZWr6XnZF82uyHrvlJFXhNWHhi3uabz4a87qJsHrNa/K/etD4b0WDg3mIGAEVi41Wycr3J8Xk8oOVa70PxOhch0uFN5+RyRLL2DCqJyQNB3cIItM8yFrIEAKA5KyShBJB9P7354CpTGznlsH8zmQU4WBBQmLjm/TlJZZHrJAZpUgVM1FxpLpBaJ7GpJMAq2MWRH9ZksmNVIQ+PaIOpgyk5UI9SQASm8+hqHC5Vt3rPciBMc28B6uwtxVt5pgtBneMZkOc0jLE2XI7JLO0UAAeIaTkYHaFgnHHhblnCk2ZrMUdxXbJJTtqJcMy8iD7rNI6J0soetQ0VlVZqS1IYZ1EIviJe8i0D7saeSFNMIfgsHNKY/0qJZDEfuOvZ/m5iJj9j1k83LNo3baj/uu23jUresg1RLj4YGzUOQRN51Utp5RJtDokl2rLU91Cebip11i5ojctXtsojHK2O/EaxBT4VYCkIuei1RagPkGSkadRSQ3BZ6OxQaH/A2+4zr9qlExwshNiLBSNIXTMwICWJgxuizxxkhUqnLyubUF473vzX4rKjy86r5VXpxmJY/czgURDuQ1mTJU1fvC4dTbjUEFsaTQcn050nPtFES6MJdoFW2jQ81+039Wf+cP/vGf5MUM/5EtcseTM5/yPE9/2/Dn/+0M5QwzIzumM9F+2GtyZjgaSPGUDwLz0yEnpB4vTs1aXcJKj6R9JB8B/XiEf9iS188DdqeXx+YalPWIshTpx1xHsqzdF5QfEMdot7TDBx3yE0hy4IKSM360gayfvVC6TeBpghce8cyIlSMjBwdEnlO5xuJDNspB7KgCLsA7abep0g/I+/HJBGEVcwEbAnMnrq1a0ohgS0hAjTioBiy01TWoH+jPSKBFCIo7MIBNHMgPGgkQ4gTeulAkHsEW5JF4cdQHlECNjc21HWnKMhQlVxpWaaKlHDFflIkyIvvZVE2IWu6mGEJiUbnqqGN75M12aMRzYldN0xjRy51bE7DIHmmGNoT81gjNEOBEZMX/MFqsNMX21BuEOiyAKrSbFvVot8VaQyOzVfOo2cgvmsJmfcAwo03mGXhbG8EAnIZUkc8kgUz38VpoP94j1odQJNfPf8gthVMgXsonoJLgJBYsqH5wkO4aHe9ZumPXwqnyJDBr665EoYppSgyiUIaAQ8MtXTVaoD7mmoMjfV2nUB2SxlNzNoZQ4hqsdUgjrANCqrTe9QqT0eK7OkZbqIIlFcjBHdQiVSPxgFNewWKgJiebzUhSusYiiw1tB3PSQzflKRwahuLSIBYncZvcM2Yi3LbMvbzaj4bq0yFtB7+k5Io+4Hm9/grkNUSHbYaziquaJbjUeFGfpj4y9Bx/s+Yd/WPyjH53b23vu+U/LA85iYlJpnB2GZdQt6tP+LzjRqNQoVwMtiaLjzht7UDsd1b3wv9JpZ6drHSv/aFoiUJ0l6fZ00rVp+Viycf/P7s9+//eyd13SM3feaM7jft5JX8xNcw1U0kdEAX1P8WMywgGdZf7aruX4qzLpLOd36MGCdCLsS7sTGp2QIgxIjBgczAtyZrcgM/3iPhDGRsjuzu3FRCQsOvoCYHLaFuOWND6ouChKdEeda0kmRJx3Kxkhmux0eIJUbYkmTWa8IfeIIPctBsyFAEmykiFJbqQVRPIAebISNmTogt3iIowZtnC51ZKyos7lX1UpfG3moF999ddIjmsXJKSf1IfMEYAVTTM+wxhfzqp7PRM40MVJk+4FK11RLJYmJK8pZw6wCcM0lReTdZ4vSWS5IxP74dm3TTwChQ+epJsmQSECcgtyfAhRMmWyqrOThYeRsZoEYay2iRhk8wAAQABJREFUGSRCjyNPk986EkNIFpLL5OfD8pdEgD/81VLCkeSCUZAL8ldinIw7FGGAmKlVEuuU7oOIJB8c0EcIMCIEleLjvjUctKFWbkahQB5hahkKU+5OgpN8OietA+lga8rvDW/IPU3iS2imZosUbth/ZFO0WBbDNdBnq4Ik1qGx3PfXU/LXLxXSm1y6xj/wYXsgr015qwHVIsrKMBZpIRP74KgZDPcmeQPDIL/ggjn33rr48g9sv+2nC4a39xw6b2jpSQfd8Zk+yjF6rVwcGxKTkjpAriofGK6coPaadZDioXf1cpkAJmFYxoOsDMVO6AfpiZvSfNZPfkxjCkLy99Z0smM8hS4dne5dnr66LN37QDr0e+lJH0m/8fz02cPS3bwu301P/Zv02/fX2ZN605bf6PnU2+//1tx/OS03gpnkZsXkyXMa2Mw0N6lbQYHAQRCDjRcIAkxarfkewUmPW4l5q2AzlXFD/GqyCI+zM9RsbU9KN3/NLG3CZyczWap5Kqoq0oj1EYLxIR8KIvoMFUevWS6ychmKRjVxGguwIlmrC9Ac/CJYlMuzRkaZ0SQPGaUakBOUZiIwjIUoCD5Ebupm0BbXACIfN9YRF4SD9SqTEgbKyiQ1hpup1bDUfFAJRR/5ayKEgPKINYk8AaZYQ+oXJAFxah0LYnZpRnHARtmGVplRlZpGZTZv3vyMZzzj4x//+OCuHGtUOxEAMRjlNAejsZccUR7TIcJCjDNrjcksupCGmLKBajrV6PxZS0bSk/OOpox5bv7T1m96Uy4gQi6HYJInuRMLLGqvEBykpwelIc486L47+EmakHTTGZhyoKdlInNFABYVEe8m6U8wWV/is3UneapEniNGkd1JDv/94z+eXIGQuvfNcZqEYUkT4BKGAc1BeBisM4R49PxqiQypsM2Q3eMpm5W2Cq+BYA8/bv4/frgH9ME6pCttBde/lrxW0mS6svyF4J3soALTIdWghFQMz+lORi93ars7+tFPuq9DcKNiGi4Unf4rgxDKkqY0EpQltyiuttCiaRD+n6X3npe+7HCuxk6m7Cfp1L9Lb/lxOvas9J13pHcdkFZhNnfQinTMh9ObfpSOX5buh0Z+mk7YDlY1kXzOSTd8tOfVh8x9ON+Axzvu6EwIzqCfPJlolHfsnGccIn8MY6PaGJaxOVUQ7xLvm5RmGZYC2QYP2Ko3m0nn1oYQ8K2XLJfNz43ekT9jpEkEsNrZavxMljBGQ5i1nNmo0DZkwFtYaprzMlTbU0jlBdhpPlYQTSw0Wg184A/EA3zrdGAOGYEY72g37+RGnmJ9Z3hjPfLxkyFnFAGdqkTcmUftB0yeS4k8JROlVJ5yX/XUgYDyK1xLTAF2RBbkhHUOuZjSiCiV3b2ciAPeWLL//vuvXr16fojRtum79qS27GnxY6zmhXEwz9KyVmYk8nSI2mAD5xSHwEwqayNn3hNkpvqMJaIZBzhEMMGcV21IiM5j52dXt/8f8ivItAdNoCVEFvgQEJQxSUE6uHaH1JOJa5+KZCnymfDC5uVT0p3LRh4d3ti/Zt9DVi06bPXafqVUiARk8SatiHXXag7y6kpFT0geZFGjBmhclRd1YTwQrFpN2JHFbu5Ssua22i5mN96ed14eYwEcqFvBOvYhph34AKB3H//xofgVFn1auvHJ6SeO/Nk6e8kRFyw/5egTenvzqmsX1eJxGlegkiBW5zjQQy7I9AnJ44qzYqZacJimgU5wWNHGM7sd/liaG/NAs8pjmuJqyeOxWpCy81FBn7HUsEmYQeYmdtFDZijjFi1ISwVuVjQIWDCkuQLCsd+Xfu+OdNxp6Ue25q1MR3655xk3Z0cbY29M/+fA9PD96WCq1k+HppUv7P38XT1H3Thct3tqNOvsSeknb+390CGL1/X0zc87g08UGsVoqnjyJGALMfshzawE2eR3x0jYGTzG+AdXmWgsggYMWIAVbZo/9vTo/6KImHwmTC9PS1QAxTGMrDtTIEXoF4tG9un3vGcU05TzkYCsIF5YkgQhGaIxwNgdTWeQCGtVQxqiyTgMWGyHmlUoSYVMeeTawIaegwAsQBB/YkBGGtPcOGHZAsJiBo2m7uy/yCTSqhIKBkb11FNxRqb8yR8tIhI1x4iwM9qg7dIM4UAXJ02xI8yl6ZzhUS7VzGFuddAw5RHKj27zQjTTni9/xhLxQTozmIuuBe9MeDtTrIRiDQdCcfcQBJLRcGQWRUvoEGTmPzFBm/rVh2iI1RWZ4uYUSG4hfWw7en36xAXp049Lt21O827f+JTrll782Tln2cDbnC0p6aNQ4g+5UE9doKohTJsfcYfu1y4n79G40gOLcKGRwFsRkn358vxdY1SFWnVCHSYrssJA+h4aKxPkwcwDglAblG9wwwYxEanaFV+l10dMOLloXrD52Xd89KJtlzuAcVZf6u/r6f3pV3ruviQd8ORNW2fxPhDW1ujGIZiigVAgpGKUIrgw1t9RAUwQAE7HKEXdoCJTg+nCnlBwn3ZhtOC/kMCvkFYc3o1dlKif6AY5tKJCVagzkN0mZSUH3XT11fmHhYxfkoEWi8Tzaog8DTNqyQduY5Ao+FPOJPplSzb/Y+nFn0rPmdezffvs+cN9fbO3pxO3rTwqu/OhdEB+ooGNAln/tp45R8265ai5j963/XDjuT7DNPLHPX/33MVf7YkBZ9BrD0c419SUcFLUls2vE+I0V05BgCzmqHxtVYtk5QuRACAI71v7XvCrYWO08Ky1T1nOvHKtVnJgBiNYKiYlP0EzjKNspXCPcRWrLz2uc7UoLMeKNvL9RMK4afwQraY2dA7rG4c6QVZQlDDKgEpGhfQgux6Mxz1IfBknRnJtW/ShNIqoZWPlZvHVhUp6ECkUVwlGpbjvpokjQ6iXdFV6l2YCB7o4ac/3gmlpP4hJXjiwTD/TXpgF+3kI1T1fy7oaED1gAXhknqszs0rgPJDoVa/KtaPg9BBb9Prv/376i7/I9WIICPmF4PCgDya4H9iF7PCVmOuQIh/Q61Xps7+VPrBvepglwBHJB2779P533f+zRQtW9p7RLOboSKCBnSNkliUd0ENrEpRV66AC1NvfJUu+851+pymSrbS+CktMUTHkRG2t0YEMeEI8RIdWJYgK3+iDTkiJFD8Xm5qXyX2omqsCVgtuxK8Ebpl8ZafUZXO/9JWzbvkTgTqOreod7hlOQ3O++e1Z7JlHH33V5/bj1CssAcS3UHFRRxCPMYmE4gnCZbARTcJrpkThJkV6XY97wqGKaohCK0gzYx9A3OEEqSXNQThMvQUZEhCbr0ZakfPob23/o1CpUhw2lVTbtTt6XHezybE5wZfRrlbZcJBBS1ya9v3jlLptmjX/34efvzYtOGz2Q09cYqPmY85WssXNT2arwQyW8UfTymU6fck95278Wm9F80nUPDTLj+2CayDJq1tFT9dSq/em8bpyEBvh5R6szUEcmDEGj+qvChNq09fexBVQyfSskJWMVQrjkF4jYQwSY0MpAVZi5Bgh5iMjUwyV6BcZ+kjso/cDALEBW6AKnDC6CB8/RZ6y1ZXGiWQkc20X+cmn9qdKnStfo5KeVYq/ZJ0eCdEHw5nIACL3AoBIInVpJnCgi5P2fC+Y4aY0ivkTF67DVbHn69e2BqSJxXBlPUw+MvJz9oN6Fn/WskJGGP8JWWGS5IKnQkGQDq6JMx8SJ6Qe8eSmryHm2pafMy2S7ZdWPSc5Nedhu8HF0nqKQ+SY4e8+/rEbvpCdMtx4kUWRlafgJCoNsCPQXYM1KqYCPuOIbhevfsstW4fSioFj//3m82679ZjenEaxkdIrkM5KnSFQ+MiEBIWQwqRzJ0S4c7IASexJxVApPwgEtI/TV1Ulsug8/Ldf7h9e71zEfA+X1e1If8/w9rk3fOdHX1936aX7FaCnyNxTPkGwkW7lb+W7saeaMalcf7YEn+mQgBKWDwYnqsJIMJaMBHNEfIk9m4JROsSURR2EyIRhib2NUcTphRpIA0WILu9VG5xkhMQ4xDoXQbenQz6aXjgnbV48NPTA0K0X7PvFBY88fPPwaXdv3U9UEpDEW2TbPwTGXIF0lvH/7l/fuvD9KT98HuR0y6CRo77Ur7uXGNhsQ2smNf/1X88PNW1FQDBYPyFOsk5wJJgmx8aU8vBolXPtfSOBB6pCAKghF5jGT+YsLioihED8dR+DA8S441fShq0okjEpgUfI6OLgE9tnYAh1D4lkXscj/hp7EkhWS3IzLIuia9PU3oxK6n/5GwtmljtqG2sMiM1igDVO07o4qZaBu/9mFyftfp5XS6SqrXeRWWe2IHMPkRHgwl5KYAdB6VBN4gkQ8ZUs8CYWk5/fCi4kJtjPxI5YNcIKfoULibzio+HkiPshB1vxgVwLUbUorbUxm/MnQJL03CIDaet+I/fNSxu2jMdJeAu9MeMRjqpHFDLFqxswNy4EhxnpAx/Ivvmth9bOufdns3pGbjwpW3lM9qZbh49RqzbE5tQJThIvQkxbwtaSpqmM/cx2CUGiuMFUU7YkkfjiTkAKWoHWV/kA3IwbpQwbxpmx72CrvWyH/TCdVEok9faRkaGhnn+9skdwz4Sk76grOEYFYqxO+EjnCfQIS5XQqN/93dxeZVSYFMaJr1oK02isUTEp4qNB+Gmi4aHulid+ygo3sK5CwlboKsNS/xuf4ykbSbN/lg6Dk/tHhtc8ss+y+Xf/6lnbDn78RS9bdNAZZ+WRJfSugkAgcWz4yeBqLhy5bHDWPS/OA/c0KUatdLo2Jv/4MnbpNxOwlpw03f4sEqiRi8oWljaEpQheB7kMD1vqAge0eaTVT6SfuVmQTmes9YpJ6wE4yTgpwsui0CKlC4mN0oKM0mKg+km36gGIBHkWQziLfQwJIouRWO9LL00rkORB+bQXAkXptRcel7kmKCVy01hVUh8/xeCsfXBqN3UuS2qZwOLdvGelXPredb3X6uEZw2ZTxWZyK2wjW5wKp3J5bndSTSLbgwYxY3JMXYLUHn7nxzTP/04ynDlpwCOfIG2BTuxhcQAmv5VFIbcLqGQ5TXWJAICoEPFETARBkJhDUZEmrSjEmb+rsgM2pAXOQS5SQlDsJZvSPl7lUdy0aBNwoyyrXsE0ZK78CXR/+a1Ya5S4g4RAf+tbGwaW/uiuvqHMC3s3n5xueFr6pdvT4SNpYEeypivLQUGmBkMbIujpb6E8Kt9MNAFGsaxQo0bFuFo1UgtrYBgQgRTsopkaYHSMdztydGcHVGIHAjUOX8cHdYX3h2zOX0Lcw/BGVt925IU/WblfWbvsyKPpyrBvY4ZpSj6JG6Ce2C+nMwhxxp+YFPqL7QqvjASsaOin0ZbGaUbNrz0pF6nTC6J9Y/mBbxxJTE1lMkphBbPPpFbo+Ogl2wO3eSOFsnxYP3nhbssOv3zoFcvOe+kL37zsvNn9hED0FEgBMHHoGFSu3ezpGUiXXJLf5aalqEEnu2RtfJisvChXd0rXxfyqPB0Ku3Kz/FVNNYdPOY5GK/9UucZGsIN3j52GbYlz1sALGuu+yhPVr3LgsSVXRToGmVCsifxu6l8GSX5t1aIi0/IUM3ikl7+akDCxQFI9+w5NOtjFekl6XamU8oNFbjvrQjVMtyhUQXC5VZALN41ADNyJpMsqxmZBESSGANMuTciBLk6akEXtEphIju4QMMsUZOJZm1JsRMNkh7gVLSs9Tzm9LCtBJHaXMHj8nBFBTDCxDfi4ho0wkOXZNe1o0tJnvlJOPuQFVviQGnhbEYWFtHVfSmBry5bF/7Hp7Cek/3TgjSBuynRO2uJsG6fgrE+Lg5NkH6+N6BnGCeADThJ1QTxR+cICbNGH28YRRZ1lN9/en5+cw8if5np/5ZHptkVp/Zq2OElim61st2lDrFlckxpYS+S16lG0tGwtqa1Q64LwYdMmtfTZgYqKX4sLcFC5P87O+9f0spenqxantVSGzw97zrx38JnDjyxQXJs1dJHPrrsIBeavPhU/xEjGJmGKgRZ6CpVBkq8siMLbHLQ+nKPhmrZDRaLlKiAA250+ReVXyCA09rilKMuwLJYHnn2Zkd54bJy+3Wvl/99bD732tmSzQPOELTDZaCnGHEOZz4wkSxfmIpxvRX7SHZY6bKWtjKDYJRm3I6+ZuQwWOsvKB8Pj4DS72AS3gb9tSA5c4WQCu04QoxSXfewkkFVgnTY5tPkpOtRf41xWJqmRZl4QPkyAgvSUC7v4STUs4Syldh0pxYxjLbZUM8KJMoKICZl32FDcWSQ21A6PCmm+BXmXOuFAFyd1wqWWaUgWuopEIABNPJNZxCsze3EOTcsnm34Q6ezcEScsm5y7fYXZVJtdf0NcLYXNSEBAhOSCUdhOSAf8dIcgpqtwI35trMhzOUKsBISKOkrgw1ROEV6+9Hcft+mh52z9/JzhR+1jW5GO/1h67WfT0wv1KY3clAh7KciDcJsPQSlPN6vkt+Q9dKPgwxHS9DJoV7zJq5q+9J2INx5aIWaRm1B1+bS90qP5pXryMjiBptYoJWetaCIqrhYqjQNPwc/fSJd9Oz39Benz+6TN302nff+ki1926rFLVuQiO6JqmjLfTTfoCSYE3Y1oEUhVmAsgCyRFzZvr4XCJw9PK9QMHzz9gDhxcQD39i/+sNs2mWS8+q1iSIlv2KtidQ4wxiRYZPySwEeU4IkaltzvmuwmzHsZg63UIQIlUuAc9rrbUbYTBNR7cY3+MbUpRW+A2FVOr8e3KK6bycbZQ+1riLS1O6Yrr0mrQRx+VSQKGmdj3Wrlf7FwTaibIqYF3y0lGr01STFNnwCVcomxILEmELQSDKiXWZNH2lj6KgRRTXrCaoaVKTDi45JBJ5kw9qHqWVVIu3Gd4vVPf8pDH1hCybYm1PxbV8GsU52JwMN/pZonVjUyqZdqeutnFSdPivD3YCEhCxr0L+o/EtJUmbuY/TIZ24hpiMsXugbRQEbcR54MYFILPeo7oJJVCCBKUVCOWxsIOYEJYSrRJ4C9hSpWGuPdUpDzimIFLN7//prkve8LWb91449zr0xnicAobgBwYmaWEimRLNnmQKHRH6SRyDaaB5q69dp9sg7cdeHxe7vbZ5+Z06oY0sU1c5lLX5NnAQBDSBz+YN6oNUUKGUy3BSercRCOcUA1m5J68Nq4oGmj16p4rhl59RXq1lODpn781D+Clh7jzgE75q39QcAwsa6XYRtNN+z8FcYJ4PY74JP2LMDBiklSpqE8DC1YLOzg9aN9k75xDTj21x1rcjgG1lZvQb1izAXfHPUI1yryWPAjjUlQmI8U5RjsAKD1bepVvrjsxjbo1Gg1OlhKGYQsA+UAMwnTiIElfjWqf3UkKtd4QlGZNopIsXqrXPHKMUo2tGlNbVNS8czAEVMESY7Mn11gsdTBZ68wj1jtmpzZkS51H2M4LRK7rUXSx2R3GPNZEhmdfiVmYrIDOEpcGQ5ty6n8qnnWBP0hxSkHEkbGHXX4iImbPGl7y6P0nbL1vXs+WB3oP+u/hQ5zPvlPQUrkJISUU5yahJGYxZGBeoS7NDA50cdK0+sGAjtlbzsXcK6Zi+X73usIBqsja1Ae7YAIblS3mCFAsta6is7E3FsEWqdIQKMSZj2syhSr111d/fch6yQYGer7y8C895Z2/tPJj6fufH1cgyW5Vaj3N5KCP4nQfuoF3gIh3QjHMVKXTT9904cs2fvOLC9NqC8rH0pKvpAu+mR7fBoIUOagPw1gtaRczZMjo2gRxUw6kZy1xB/AYVggzQEcBNP7aQYgxIw14V0nmK48eAob4WY48MndviVfCB1HFHhbHQKeCaK5ZdxhX2LRsEcIxL1H31M4lpUASqqEX9JHxUChsHW0YQMxsPO3pnnTo/CxXekAwaMLdMx2CJDjdRHPHKVmRldf3NljaBzEJTmrAtYZ6b1g4GGNEGUpfbHPzlF6ON+GwEBu9LM3SaCbeGr3QqnG+65ZGBhhUxPDDjYiTbchwwnOA0jxC0KFNi7Cp3jdUdFAzuckE5aQPE1Bi+UtcM4Oan2y8WO3yy/MdDKLreDnZTZHJq5ejo00QQ5FjVH3cMfAkc6FiittZpM6kgYb4YJT6mAha4b55x/Z2+rbv/8r2y49JP7br0auOr+m56OPZ81el/aYPlaI7tC4uNM2oYMolEkUFgI/Ns3tntbqbzxQ40MVJU2DajkeEfDKHkIAh7FyweZD4vwiOsx1cmPYVecGkweDMyULm4iHxYWMQQR8LeuJMlACVSZiSX361BPSTB32CICoLXOrHmoycdcgKO78zMG04ssyln8RMUP/y4XPRUySRlB6hrmwXpyRqaO7cz+77mg+PnPL4dLtfb0mDN6RT16XxhxfVPJbfsoe/VsH4Sf21ZULSfB+JizYWj2AC55SoHS6JIA4f4ltUqK+CdfztTxtH8pu03BiPRtPmshgRyhzE3nobsMkdjGIMWL48N6jACkohu31cIF3jLACWiZ1LJosTIPW+zi1zTBcL3VWciwnpwXTgnO3Dh8/KjUmFf6fNU8aYsrC3lvBcAjHLdKc9a0GUGscrGIqfDS8MtkIf9PYsd3lRWW7YkgsbSTwFVpajoIKfVgU0MemB23QzQLwryGGhjsIqAqjbFIHz4CnWmT4sbTySVizOUwA3WYacm1qMkOZMPGIwOx3RBAS1TbQOQSpTn4+QI7jchMUQMIsglQ9+nnPO6NZOg98AELvjvi7biThJW3S0QmVrbNvHxwApktq8sFob2Lr+5A3fGhqZfXs6cWl6cOnI6pdml4u0vipdKMi7mQ+TvaNdppVGIc13jXvIvCOddi4ZYwyc4GaZnA+is7rUCQe6OKkTLrVMIyCRGg5TtnFvqpM1jLehV1o+1v2hiQNEFRUCspCDOEksAgHUCfyECA6yWBqCjAKj3jhWSE8y2icEqzU65OQnzLeO1wuUBDMJKcyjx8wA0XoKsICWlIXAL1BJcewl+q6WLr9q7nXpadelsxprv4Y3qDbd+JsUz6/92vhbpW9qSHI5O6C90I9d5RwBIUBLGeSX0IwiGOEKA08o7yLZUOIPpsWrIKlIQBnQiPAWdVWQpS0WEakYzszAU4PtQXpnV8S06gVOq/LSgvYSAUNV25IDOocuKWrYaFHVPCJubdPIbKPFBohOrBoAn3gX2r2WMNymSN5zIYOveU2uxhQq4tYYcDD4fmmN601pzpa0DxAbObC+OF9xQsJAH5oYn8VdGYrayDAjJKVyxvqEWbVPAO54BzavZSeksaK4Ii7wve/NA+cbTc7njtnBVOYlJIWRr5KhEWi6AZSBOaw33v723EDowU7I/DVZYjnkEdfILC4clHofr9wxDjvMs5NyizTECHBGPpA5hLkL4zyvw4Nbrs/O/GrP8vnZhlOzH/9SuuGYdNuZ6ZufS8/cuDNwEnZpjuYTAj4EkcEQJtXy2R9FPadzwaVO5phlTmaJ2UQ8Mq5bJnWpEw50cVInXGqZhtp2BIUzFU0w0tl2CY4DltsuTY0DBEdoZSLbznBIwjoPY6lSogRjMZwk9dVfE95CnH6l7yl1ktQH3BkczJXrJZfk6Ed6FJqYpOD75xnhUgmZSyoRkcwArEq14dKeHcNP+aawzhulDtxVrUjpdLB9kQJZ2hD14GReMKiCk0h2ERsOv9QWHCPZreCvv74Ww9Xe3FEm0SwfoC0MotayXpgDPGEUbIpRHHO2Z8FkgWMk2OmkXyoOSrYZGDfCRFoUpy+qUElKXdmG7eWsTFWjy6EyjC40VploKa8NhreMLmfM4L9tiYwrWeY4AL69Pq9XdhLq1jQ7bEvlZzu51mVGLyCif0EQNjPM1wtKZF6aGpkdxryXilgtyJwFiB2uQ5CkZwFi41zF4GNhc2Vgijl8ZLhqQklQIV5Fb7ymgIPAHXYLveCUI5Iw5nLlkcpX8ItVw8EBCjUS/DW8bfP0eJBxqHU6xTQP9FbJYTpftc5HtnpZ64AVAd1HHpmP/77eWdt7ZmU9vatG9vt6dpbYxMG0YnF6tC9fe+wcUi6cVJA6GBLaDjfvdLKkR+RS8NCMY0/qUocc6OKkDhnVMpkFN6O0DTVGPH02plZbpu/+0AkHMJMjgKKyTg3zD8bSBOQIYYpIN/LdtLc0h6XINQQV6QvhLCS1v8xRiOYgFKh8eVJIHidwIadABiQ7aQ5qtCIRGET/pEgRMFAF3FRyUB9hNCyRFHArIripDS0tEywoppVyol/jJ01oKCrpmvRY+cm6azkwBlhWip5WbeoKJig8R3goLAlv2QxCqorStSTYuaSXBe4UpGscMBQdWtysu6hpLLzbIRkMDHXaFRHihhA0IHCNm8leLQyhv/HfIAFfnK4OiDP/XHZZz333Djw61D+Sv6Wk4XbrsLzxyXQZxKkrERDg2tFQzIfwSr3/d/zjlW9yYJQCZ20OiCHhCFBs7IQ0Qku5nIRjs2mZMmWQVOQg8hpylbhMyrr00prj0c1TQPOjH+3IQsayy0ON82y9GqJf1EewoJkepAtMzwjqitaV6zD9a40y8iEk3cEXz9BiDSPGfGSWBdpWB9UP9M1+bGifO9LRG9Ki29MJW3aGMUm18+HTCCRXjAqQbz6saMTRrmhmMMqKqEtT4EAXJ02BadVHKOxYbVd/6H6fBgdIRs4RG87IL9KTPqPJ+Eo4FMjTEC7kGrGC+QQNwMTtInH4hiQgXoEkoTZcdWHKpgPAF0oRFICxPAsKUI221bQidia+VHFOnRPry4SHcSuaOKbbeHZaEQ2hboUDIpJpPoUNJOGJVpO2GgUXTplwg0pjewCAGKgqnjUiWwQJLOWcYgrM/iYYIhT8lEusPKhFetY7cRWBxNNUAikq6dt8peQmRRbusXb3oHYZV/oF2zG2INoLcd2y0Li/YKFB1cusNGVl5kE2kmYewgri5OBUS/9JyRMLCeejFkYdtS2QbtGKyoXzw8RQs9yYJpYWxlL0vpcc11KtHZF/VkG1fODjBvvKTtvabN1UAYtMf4Fjg1BBrh2QjeEgIz7AsvCfiVxbUKtsO78f2RZdr/eNAZ8t/fvcP+dxhwzfM7B9s6NJLc1uSid+IT1va91LtWuLMzdRq2oHTpJA0ZrJ2Wc5B0CbzrVQtbaI7s3dw4GSPNg9BXZL6XKgYw7kamlsHzWrg8OlBNMQoKQY6zSdTUzzMtA6ZCuvHJXGokDWkPvSE3lW1RIwIFEGtAI3ViwcKRXhCG4yKrztbTnGakWy4poRZWzfUJmIOT81azvBvzwXDAMTkkKdxgwf1PpHtFFWVAi8WCYKQ7Aw2apdIYjHlPr4xf7oM6M35ca7Qf4K+mkm5gefVgJdek/hG5zEG0K/MvVh8s4i5bLY0RNMd3pkyu4VbbzkkilWyrMGTC3hjN6HRYa3OyopDczpkbi9JgPuWQqbx0aRf6ufAERHAoqsB6Db2yOLrFzoizJIKv/U6toQBZWgeXMnlhAmC6BmUnROli7mVCvCAXY42bYnlRclBh2amza1FYMQbgO2ynfa5zOdX7miTEaLLtOKYc+yocHPnjU9yx5Miw9J9/SloYfS/pf1vPn2bND7bzosq2hLc3qT1wf3pCG7DCeWPCOf3LDYazUUm/Pp3tk9HOjipN3D524pO4EDEAO7cWE6prPZOYAGAg4egp8scKlzQicimRRJTHvKr8SxeJdYrQq2kIBKEFgmQgXkak/0oshWQp83jSRFAIqbrv0VssoGo1CKzWnsnAiheNrnGb9SqOKBBHNQKgXykA+pDSGJIBYqUSGoSAJFh671lNY1Yg4CEpW9dKMgiUS2YGUzU23ulWbx3XynUiiTD8eTZHKA7dSZZyRYUaTsS5vnpa24ArVuS3M7OTqheFbOPEdUo36B5zBwChYykYI2Se1cMqJe+Yr06OptR6e7n5B+uDkN3LDx9IeT8x7qlSX+GFROT3DCUJuNZm0YDqxAouyIrJJy20Vk8IAg+pSptSDqWXuNk2KLX/GTM7ibK8NeW368SBwXhiXdryt9DBUD1WRB2g4ZxLWv0rAmcrpVIBG/2xTGQBTd+V+NUhnrKwzRfPNIbBzE5mZjfvWuH55/azq+r2dke9aTOTZrkhRMi4kTj0bXy5zEKO5HcXCSwe9jwv6ike62AsSTIHK7cym6G3jVxUm7gcndInYJB8Cd4uVEVmZcUaJKLQT5yGAjsoZ0JonMN4YQKoEYgjAId64W85Ck5pKznu6E5MbyRHWxLpB9PjL3oZjdoQMgMKYv8m6yJFD6n/5pEg9pjqhtr6ElVlCI3dLzNaoVSFJ/kIvy5lmDySZLMOgVV+SGN5BUG+XjkAWhxwW2OzQ9cFH6whnpW3PT5lvSydekZ38nPT4O5+ywLL4V3ccXSYWMWcg6fHQ0GVZQujS37p5CR5QLUwfY1/AQB7Z69dAL01dfl/73genBH6fTHkzLHk0Ltzfei2cYGEVsADwmdK1rIMM7Ohga2QLb4KRyWZVrPcUXLDyL6dT1riAjQQ3NEQOj0Ey4R1eZHXYaem8SG6clB9I0oc1i6ULll+sD1JpKAG7TIMxT2SLHaiVe3gY6licgWGJAxIyTIUaxICqdSViIHtbVZiIf5bb6qVyZKV/LX3eTD9jib4TVuxnLDz+pweKlOcQzHkx5pLb5/QbpI5wUgl3cGf2h8V8wTf1bNQHDCSWcN37kw7qMY79oRHha8YppK8yiXM9eU9O8StxTnOnipD3F+W65O5MDcA+7jg9pZYKBEVARZeDDZmPrHLOTfbBEEk0fapgIiyVdh/UQbGGjtQw5oUxsQk3OXqTqDn3js3sINNE0grUieTWncqeojxqyvbGHMXWwopHItstpu0dcYx01NiEJH4EUxcEg8fJYQe3ZriVod2jtY7+X/vYZ6SvbU79YsiPTXcemW/8+veXb6bTG6U0T5p0n0CJLeYY0cJP5agokCJ3fSjcJORfqxCQDME2BDCFdTMeLA8OZw9P9r0r/uH9adUs64ePpZd9LJ8fxVHLGcGzEQwcrGHtC2fgNKVFkm70TvDphbKWG9K7+Qq16s5LeVxwTyae2HZKQOF0vgBretahwrUQIBj6jpOED4UF8T5gAGRgzViO1i3sPGlR2WtQuNoI5TuoCGcuEP3oZFFMNk1G/W+SYUK2ocz60yqHNfZlrL9KWWEoF9CciAG6dG256iNDH6IKePVJUyVMmvr/FnUpZBa4ySFzHtMUB0qPAYcry0YlmIlQqt180stT8sz/bAZI0X6jDO9+ZH6GHLTOBujhpJvRCtw47jQNkkNgOCxGWfJKIzYmgdzINGwxviN1bIYZIQDLL6s16rkMiy1gOqGGQy0nNlvuUU7x/qsMcdkoy3kNqjFyuSGcWFCK+Vl4T0HAD96JHAB11JpjCys3XwJDDj1b7YLnClKinAidhBTMYrlr/CRrLvvT9p6WvP5qWOInGI7PT0OHpznPS9T9Mx24cewNxOatW1/SQTVXMDBwuNLcqTVirclYsFj5BGgvCOtOZSp4sidd+97tz2BGln5RuPTTd/XA68Ob0xBvTSQLDYJhynmw/IqsgBoqfOjQ8KFfj0Jr44x8vJ5z4WqshErqB9mWOghd1q8Gsy9qoT8qVvQdM7IRdePLKV+Y1ASKBFQTKcPYJ54dXdDG44Lgd3m01YX2EYGyeYEllAUKQn3mkYsqi1+HvAFv5byWK2tbWBwyFqww5QI3XG+Y2CMM4Wsqg3SUuYW/D19wuWYe/RVXliYFWIBhOOMjfnUAwrtXZ5HJHi+JTZA7XegQrWkE9mcghMgmG6FwAERN4OQNiuo+xcPYvoDEJJyFyUqhCbgpcEwo5E6iLk2ZCL3TrsDM5wGhkh7MwGvLONSmG6BKH03zgA/kSOeQdAQ3uWCZ2QgSoU7yZYegMQs1XegUI2w3ETUZ7hTyl9Ylsi9poQrn0EO7qJg1VR4uEUJaG2lZtotm5lIODuW8r3FuxdPbWCEaFwuJdzrN8TR9AlgXRLgoi6GVyfLpzIG1tRO3kv29L/SwuB6e7VWRSOMmzquqDNJBq0aipEcjleCSgtohma5MP3oo/Uy6OGTCic3gAC+7NSVvoR+dIPJQO9l7jBkiqLvklhh6AJICGjY3OAzLK7GpTevETlkJIGs6J45BVmehHbNcKlktK1Ga0WguZlNwWnZD8OU9jRtDfTG522Bkbmm9eaAUU7mO8sckBScY5s4ozLABQKJAV5C//Mjc16XckHxF7YvJMJZmorTyNh+i1goHNFdMo49Mrw1klhS1byTjqonMy8Axm3jpzoU0pHWaI7aoN6MjKzMJniMd2EB1hgWEdBe/qAv5BvyLpg6Joo1RlLJkwrXkSSRzchkHl5qunVBvQxAFFxK+Rp7FHTHUpOGAKQ/BdnNQdD10O7CoOkDtkeoWs+Ek9Ejl2unGU2A1OPk5IRJsZyzIcwEJ62IJlmOwThLFLye6b97wnXec0l0ZgBAXD3OI6vpaLJnyRhpPyIXbLv5LyqsoCBNuR7MjfaLu4UQYGB+G0WhBHPhQ/jlWIKmWKODA3GvU2XsGbe4zoF9feGAowVdJ3/jWUyoTptTTUVXNKBkWIRzg8bVShUIR6k4aTxk5GR2mHdpdhBZ/dno57LC1m8lBU48OaNKYqx+drVARakgMjU0Db8UlafsNe9VQlg3Pt2uyWm4Z6smHAa9PwwHe/22sAnPWEDeef9tD5L1yw7JSl5Sbpdwawz3ymJR/KRTqnisovhgd8AOKorU4ssxEMEksed1h6VAmINE48yB0ZI022assspJnijegzGE4+fCUTBmbpXI0NGxIc5j0zvjYP6XLNy9fYEqMXEi2vB9oMhvLjlWtZqUMsAxhNrZ2Y2eKoVaiFy54tTQJOYWC0zKUojo3Z8gNO4l60L1IOBbGTgXRQL8Ir6QMgamzUHPOt0/zkjskoAWDapeAAnujoGUJde9IM6YhuNXY5B0gisp6qIJgs4wApIrITokGFaHiqTCSmfWqBkzj47GwXF0VqW/rb103t7RSieq1TC+ksf6HKbUjKVmYM8prDjqOkOCOHZrIoF1/SbPSuFEHiUwPNazvOR+6Y628/44G07JB030PpAK+XWJQec7TyTelJPHGVfCb1tdDHk3qqSBysCJVc3MQc7+IQOG+1ipnsK7ZZNc60DFeaPU1VI9at6fBPp1denP7pxPTTJWm516AKw4JIizyLC89GnkT8pCpPQUIbtIKB1DM8tGT9g/tlq1kfNmyaf0pa+Z89T3n0/llL1n3+hO99aviGRdtfenbf8y4sLA8aWMRcFzWpvVCKc7o1uUJsG2HDK9/XliD5qxUjEz8I02Zzuwwqaw9mIfXXcFYlBRWPl/MsroPJTFahC0ENrqgOYaXMmYpZfZjcKps3ZTth0VGHcjJPaSNyoSZAm4pBflZBYA2oRG6wEZqJGFWmKM7hapyeuAodWsNw+JpTJIYPhpg4uAf9BArXTBFsEpuMDHgyh5Pkg4gXsFWJv4BEGpd7JDgg5s8RrzOEujhphnREtxq7gwPkshXeZIkgE3TSTGSfn2hcUTugRthj6BJqmP2JQJwm2UkktxCj5ay0AoVwL9+f8Jr5h3QuCLbzKi4uBiqqFboi2R335/UdvA+0SIXAQXDz7nTYX6Y/fXv6y0PSvSxJj6al/5Ze+v/S0xuhPJUnJveV5ggFU36MeyKUt5tY4bqZRZGe1ilDYSkpPOcG6c3SI4EIqMoC+hQXeTbeUnJ5eumdafDU9L0T0h0PjYK/cWmiOH8VgZmdQ3CP0BAI/3OQlIYPSQ8vw9GUBMk4D/zodMdJ6b8/NPL6Bzfv2zfr0Y3fvX9o1S19w0O50YMSblCHyhVEZvYII2I8GH+xosSN8i87riVgKWkAyh03i8c5pMIn5U40p5po/HeYgCUvwsmlB7WNLnsv4nE8bEPqb31iDMjBBKmQekYFJmxRPCgx5KfLYpgxJvGcUtvx/scic27HIl6tuOlCR8NVDGlcnypDtrBY++hKAgHHtLTcHD8JlFQ3hbLSER1gJYipRBOTebtYw5RL+bm/dqIH6++//uuOhhIs+mL6InRHjtO76uKk6fGv+/QvNge8UYs/jruhIAJXGDJIYfI7dYYEnDJxf1SWsJFVWfKOz3zMCDB6t6rL+VzoVmLax4qWkYwot7q18G3GSVSITVsiusbU8fiixn3rvT495YXpU2em/3IuwK3puJXpiBLsGJd0Ul8oIZ9K8A2tRkfyIVJp3GqxM6s5W/UHIEJrxq8e5GIbfzhQwbEG9mzOJb/TsyktvCad61P/+/i7dGTgpMBw438c/UZfcuUIcaMvyxrdFsyFac1D6cC1aRGIyFfz0fSS7dkAz8zPth/805GT7s0OOeCBzU/79E+OPvvhvkbHAILsH1pasXdWysUxge2UdJkhkQby8DhLWHviJtMXgGZ7iuawPlo2wKPUf8UEZbwJSHLggq0AQWwwhqKTPGXucTUs86RS3B/9UW6GsX7wSC0FMq4wtjmlZHzQhkTU0BSAPB1+hlHN1Co3xiQRWkhj2eRkEpXXF64DexkMOMyk5KZZFvKBVVgCzWR8Ij3AMptJ3/CGfFTgMzcr8xUIJVRgansRmpswk+9gEbemUEJDlMTj3+e/bgb0e7AJXZy0B5nfLXrv4ADZRxXVRl0IYi3J9ELp5gDFceF0j3iOskljUg32Jg32f4vOzqgovUjuzjioRBLR0I6ItA8rqh11oyZJbUK8aAt4R0Db9tUBSBotblua8410TlH2ZC5GFqQNx6R7Dkr3rU4H3pqOWJuHS+fAhbuHDGU8oM/KRJhSLaLRyVMH3tRqVm2hcqAWbaFyKCT+qSuvLGfT6rrKt1bpau9DsVw2QJJy/XVdcDXSM9GpMG43490s9fFgOkyHSWVr6ve16MEbs5N/Z+1f7D9r7Zztw5d/e8OrP7fk5Qdnixb1KMIBDWx+NGsFkRTVk4Y6p34qQ1HFMDZsM0BbezIFeLuMik4ICsdqQOSyy3LHLsRvjJks9lJgS7kaLDFObKImTRa7MnX37/xO+uu/HgdnsYtpga1FFDlXL8K6ClejVoYx37qpqt+DapP5SZ7ADXRiqYADVjvLl+eer0mR2gpyR9CVYCMQHBs1Fhjy0R0476+mqRiAiNvsTDATewmgjxVKt6wCGSF4c5ORyambXrcXY4NZmtvdy/VmjmVlUvyZVGL7lH1mJk1yXMzMRnRr1eXAruQAEU/W81kU286L0sakMM2aewugEob8xvk6vUTk176afeWa4UMO3D6rN1t6cP9+B/SVNUSRSasLIpgcd5xjePSakjUDo3KSPJh6YHbv1qFZUUmKwZEkdhiN1TlPTIiT1KQ2VeFa9ThBuA+cn+nYt058lBRAK5Kbpb+AMOrEOyjoAHVA+OlizIKVHZ7ueUO6/Jx07by0GUS4IZ39ofSGO9Pj8JKCcT5TLfEBQXsrV+a6TYtqrSn2FYty0DTvCBOW5FSIZmhSm3ntprbmlPjDiiBPfY2KzN10h5mEAc9u8wpR4YUWr/yUpd6NaR7jjpPeGweal2Fu74Y05+Bs1UFp9drhBZddOefYX+oRqaMg44TxEj/F6wAo5f6VP1bDN7VuUziSerZRoDk+qVIx2M76Pg4UqPxU+xViiPieN785+bQi/WtrYZyzCvj6ajQCfAKeBEfDE2oIqYMghq7deQXpdANvbAgVt/NwH3tdC7tU0SORAit88MfjRoVegFEMe3Yy4IYVh4VSjBH40gmpQLyRUGL8IR8EvzMUyVwpKCqJb1KaYrrJr6YDH5NSmI6QVkvGpmUsqYmjwDXffIn1CYZYBsCRbXiY5zJTSYv0YISsaRGkOCkBOHOa1cVJM6cvujWZuRwgf62PBUYgk1+wQhGQEQgp7B+QC1UHoAgvsXHqrlu3vPH5jw0Mb5jbO/yCV/ef/8r9n/S0BXPmlpXfBE1+zWvyjXV2vTVRG5BkW9aI94ccmVaee+Giof0OFPnBANMKTNAlBBnRDL4wOQjfoeCt3TuUaNQDvxg1UyYqjTHM+tgFpELxOHSRC4/upBWUWChmnqaL09XPSZ9fn+Y7WcAb+ZxXuSUNvCv9aePM63Ku465hR/oGheYb99vYFzYkCtt7fJmdNJDW6Zxmp/VD+dtXRiUkOCIHjCpUr4YwXYBr4T4LoKYyGovVzAlGCONBBbVMWAEgyR6px/LdgtWtmCNpYM3IwgM3P7S9v++h+2YZGJisOB9WFtzmjcITiFAgmnJVhj6GpV7yktxBGcq7qIAaOueCa1ijJiQGD3CqFozWPqtinVgi2VfUVr+wl6ieEYjPuozlz8oE5tODrGWaViGWKqCN6aVCEJWhDrUEEsWE4IO/ckZKgUgkgLGUS4sDMUy2DqFVugghM+6003KWlsmMYLw0kMpkx4a1REHsQ5rMsISx5o4iMFYRsLL6q4CyADJnUxkY5clFnnhQAlXSXnOkOP1BldQE/PUmvkr3FeXO2AszhWEP7DMaMRkDgXXI0sTZ62gvrPJex+Nuhfd+DhCyFuXMEkxKXuJh/hc4iQGp0KbRUBDGjpatG/ZZlK3rH14/O23dPjLrmitW3/P1e//wk6ef8MS5nYs80pZ6lp4Y7ZzgJK6q56d/f9krLxpcPhoh2+Zx0px0pmOIY+GTFSXR5kE/2UIFKFxyyQ7o46w8/hH6oCwQhTrRB5QfC5CyiuYsSBufnr7KjNQ4miitT/3icp6avnFweuM96ZD2RcevRVbNiRmQ4DNmCarR8r2pXVWejrEZkB05Jt0FqK3oOTxHS1n+ONuYHCz9o0Sqjt2CAvCTpsEQ7hw4Z90xs+6es23Tz0YW37H+MGcxNteq7Z0e254YSuakPkFRzSmducP8t7rnoMc290NFVHI0yjih3aEZ1WBFUzFK109HHpmWL8/RUjNBISwo/nYyusTQFNC2OavmO3y7tYVWUlKl+GlyFTNCnTEzYDf0YNLVkrH6lrfk5hlHfxWEAxChVYFfIQwVNtLkpl8YdZQCW+d9dGD+US4cY/3jpoIkwDpIiJ0JMpNDmcBQb+oVjOj8heCYAW+cm5sFcSAa8BCVEpWlIVqhhkpxX0/5KwRKND0TIzwkjU/UkA1MxTDBU6g8pF2r295I0Cf/qZGj4VoB/toywvDJhLbXURcn7XVd1q3wnuEAmQ4kCdnhCBsnyBqRNEWd5qW1/G6iYqCnDWkeVez/+Wm9mJtNK1ZcdemDb///BmcPdGpSgmCUSxkTo0Rts6OhKHfsItf9XDZPTj84YelDx55z0Kbt9cHgY+nz/0l2mVNspDyhNilSMU4BegVa4lkTwC5YmF6p5GNJ/Zu/md8nKz/2sfxYwqD5aevCtJZOLwp1PT9t3Ce1fpNFkXSiC6wDGtRElZgZxnu7AiTF39HuYH1o7J9jEczWp8VL0+rHpbtvTccyQ9B2VBrFVpC+CE8i3QYq6aMT+1dcNPyFE9d/rz/b8FhaekN62ifSsx/NX5o7CbK97mfp0LwPcyqPk/wON9TGbP72bQN9/b1ONKDs6fWC9AWdKobaZ0LCDaHTyFPGc3lINz+r1zonVWJWqQyA2sehGWYY1qOAEdJALTAf/9eEBEKxULK1FGT0ykeoEycXPuAPt5pm6jUDAAl304lIsry/TsxxDAwdcMcj7vNzwTd0eQHd8gdSbpDjkgZowG5NE2tVIdhLVuHF9hN+KgJ0Y1KVFRORAC/jH7fFipkCio40F12UfJD0DE5mkJrLSrWNOsQJXqlMfnfGE0zJnmo3X4wEPcudKpAONu1kbMyo9k1SKM6ouncr0+XAbuSA5an4A6e2NGmUHcpzTlrn1aLr05yhNJuu3Zpm2fjiKGfXFLDr733y3uG/PzwNdHZwU+PVXVa31AkZiix5m0ovs2BkIG3z/hJQ47Ge/Y5738UL9p/z8J3lBC2v5UxDeI+ESNvYwtMyadMPFIyFMuu65SNzURjbvSjX0plWKMiqnZ6wB4oHs2jFo2n+Xemo49ItG9ICniL6hYXpvrTs/tTZQelF7q0vYM3w0IGD4ymwyI57im98Mh62NWnxY2nRvGxDJFJhSrSZ3AeYfJb0PPaioU88c+RzW7M+DVmSVg+mO6T/cHr5JM9H6BkpocZSiaoGPdn7lv8HAtKd45FfKW0Hl4JFuFmBfllNSEV/TZhSgvBAdZLSyLFlDNaBw8BNNdFZLJEBaybMgf3MpyCVFN8D9OgpuBxcA7liKwDkZIT7awz4yVC0lQz+8J4ck6sg0ARL1UFWzdAEeGI9bUV+haLs3oCezVbFye0FL8gNtGaBO+KT3EGabFLDEKDD4GDeWBgL+dW1SHmBdxCeOnjKzOLaVm3rEBYaTcNevryZDzXAX7wt6qmviVBHlmtCcTNv9t5AXZy0N/RSt44zgAPCCyCA9kqlPz+Heo5zCAGj0LjkLdvSurRgn7R5cVr34MjkrAsEisPWIu4ED9qrq56enr6+Wb2pvzfru3fBCUec2+sOUUXit38wcrZQBgRtsbHTXqEEdOdE/Dltz1GEgUUoBoYcb8MQwRO6ociKFrFcLmhrmvPp9PK3pvcsSw80PEXbhlK/O+u5LncGRduJZhdqovtKrNBFUFD+l/PUy0n2TWv8XZDWH5BW6ThQ6fY8nLwjOj675QnDN2xJ/RvTPp5VlE18T0nf/ko6666OM+moJDVW5ZQbsSzNp0wetz9fIFoEM005n+YH4Wymmk7IOAF0vEoFXINlQTfwhXEFPpgCYQvrBcMP1xusA3jJH4sgJ7BD79sVYVTzl7kPKuGA5tsoIEGMEyAJBAGnKoO2k8rIU8OhAUspIxxsci4rkKQCldyU682SrFNGo5Z6sCC10nw1BPiAb7YoRjLVE+3u6HOzzCP8v9yLkpURXpHDzLnAAQZ40y2GqzmID0Bqub0zp7bta9LFSe350/21y4FRDpjn1n8tKFe33rk+3Hi1GS1GTZIP8UIn26FsYlqSHj0gPXjiG86d1V+Siy2yK98W/3jFFfneYMgjBGVJ05cTEkm9m4dnL1rQ3zvUs2BJ6m8oGwZ8i1o28A7JstXH9jTLQYfctCEuDIb0OH9PQIxHCoKWYEp2o8HB3I8QgjJ+pYc4F/waxEX4hfTMTWnuC9KnjsljGE7+bHrhl9I5jd1eRX5Tvyh4FeYKuNOHOhwezuFRAyQBSiOL08ZXpE89O335I+kVzEh+sj//wXRg56YgES9L+tdt2j47yxGySP5er21xGNKStOauseqLO/Y+tcoZB2M/Tu5/yoaOZy+ZMlFaTp0QduMlvkLuCkbVZoh7DCSdEKDD6wcKdEiAhTfpQjCQjXECN0wNJEVxcAaPGLYYlqwX4Nqv/moeO2xgQRvCtGGgMrwQkCcWmxFUNUxwf+EPCn5qxLkGKj3/+fkA0wpMwLfy4C+yxfzavpMYWFRPhiU9oqpSmvXCzAX6sEgZvbylNpQceWQeGT2TyStfiCz+bjwH7/iISQMQUBP2OtrhMtjrqt6tcJcDu5MD5GyLsAl6MZ9HttSI4Gxo3/wbI45NZ/Sl+wvThl9OX1+56Mkv+539ic425IHiE6qLRiQ3QSULbiE+IX97hdBUqaGebSXb2CMHC1lCHxHH/AuTJUGpQoh44lqRFbOt2lbMFK0wkXI4bfGIF7lARSS+YGrIKZpjpxKlWNaF29K8a9Kz3pQ+9Kz0H69LH/5cumBrKsXHFtlN6SIKxXN40VrWX2zhkSHEX/trI09aePc56esvTp/8+/TmC9Knr0tn3psO9ir6lelxt6Wj1+bvrWvmc3091qSF/fPn9KdR316j97dvSfPWjbXlIx/JT64SBWxH4TSJNgUIxL5Mc2mOLVxFbBV//uf1ajvqqZTXvW7iKtN/v//7uTVlsvpb/iAFkKFR3GQe10D4QN3CIDFx2WMpPGWAMVDZS68mIq8NUZYbSwWHh3FvlUGSh9g7nYwgDV3OTYwPUJJu9HcAAEAASURBVE455Gss407/N7ANM6F+MnStIVMgrdA1HndhLtuWCBsJ+VJ5TIaWREE5TH+GE2slDKohIrHsetEvTsYC76bGkz3c2OznizZt2nTGGWesWLHi56tZ3dbseQ4MDWVf/GJ23HGMEOM+T3hC9trXjt5ZmFaL4/Y+0960fVba6mJW2uZswAsO/sFvnX/LrT8ZatOM7duzW2/N5swZzer447Orr842bdrxxMhIdued2XOekx1wQLbPPs4YzPr784+vCxdmPT2jD7pz/vnZbbeNPnjDDVkvqDa+zp189dTb3raj9PLVtm3ZlVfmRXeST6TZd9/swx/O1qzJs9m8OXv727Mjj9zR2M7zmWxKrViwIJs9O1u6NNt///yDbwcdlP3f/5sND+f1OePwh56evnZmun5eHlu27qh06+HprllpU+O4B5E7Ph01c1Fa/eF93n7X/qd/JZ37xfTsa9Mz/jOd8YfpUmNADu98Z3bHHXlxCr322o4ybFXurFk5304+ObvxxsyQ2Cl0zz3Zi140bpz09WWHHpode2z23Odm11yTbd2aXzRXyaiLj59OOSX7wQ+mUh08Wb8+e+CB7L3vzUdyuZQnPSn7+tenkufPzTOYc+mlGT7gf/Ehc37zN/eCJqr8ffdlX/5y9o1vZKtWzawKr1u3bmBgYL2R1wHthSawPQwsu8X/gnLASo5P4b3vzQMFLO8YBgRDiBWwdYXXycuJGNtF2MzL9WIvu7v9yIwK+80bftXr5v7m7z2Bs6k9XXddfr5IxPdIyVPgtXFMNd6lwJSFrMOsxhzKZ6EspsT621m9oiuYTBiNVE8FOEecl+NQHGENiEPBBhNL0imQbHkiaoljzg5/v6pSGGxqk5VvMlB5LQM7vBZZ1js4QDwTP5SqdujQKefGN8F7VfCq/FPlGuvEvcb2bEvb4BVjm81HFuuYfNfmA1aVYsbvSvn+MSbAsXw6NQisTft964RXPe+UdNh1P7h/xdb1ab//SM/95/Qr4bljqHD+EFvFn/xJR7aZsdJr/sdzjWKa4k7aWUtzI9lGToPZSDawhdQYRbYxlvf2CyHymj8JxFxjY3O/G3IMn5Mlg1PvOHHjH/8xN5NUyEZ9B/DwD4ZxtPLrDP+qaXx/9qyxDDXH5Ri9XI1saWZu+34U9G2scnMHE8w7OTOSzXxSbU7MKfsxZ04Duzhp5vRFtyYznQPgCAM+NAMnCRdgWA4TPZPyb/92DpgeeED08fpFPZuPPLR3YN6sU07e/qQn97zqjQOkZBuCIXi4XvOaquInDb1Oga7iJKLjEXnqa/FmKwLIEUEEsV+JJGZ5GlSARYAk6b0Mi6SeGtGarU6vAcjETDRryvYFSW/PtrhdZ6hQD2GBD5CkXZ3nJnHnjaL1bSzis8C3wcEcUGoUFvEGIgzk8RFaXtCU46LU6tRXHjf/5X9025U/ef8fr/Pe3DvSUY2T2UfzphQ/+ck8ogvKnBopQhdzTnEhgSyBnitZ0abQBlUqchmX8Fl8GO/nhL4kgSM8UHYjelzkE7dRhQywq6/O3T2aIH+AyYUhGqQ3bdRSXIWk1MUUvMrXkuKUy3tbZFVJphQeZ07GvYvEMnIaWvxY0uiC5cvzzZ7wPYKQRO0YlrxROAPHY3jFIVg0FtvBRM23wQIzsVEXA0k2wXVpt3Ggi5N2G6u7Bf2ccIBoI6fK6znqVkiEsJvGmzEWzJ694LijhxfMT6edMSCYoD1RD+xDzAwUWzOxmlhqs2PFzuFKAkddk8LwWRhXaGLSlo3E/jhbvtWTDWDKRA1TyXJuVrGifMCdcuB2h6VorL3NlEQ0hxaZAnWOqCJzTQAlxZa9/e25xgJqreCDrNSf+cwkcmg6NDttYrE6JK05ePW8jRuO+MIjZ/57agEK0hRBUoAMKMRwoiCZ4kCfCvKgfaleJ49Tz9rrr8MMjQHhPnAVPQ0JMWy0Jwq7DXkcZPfRBUK/4wUj0JgAOKUwf5YJABJMbVSrDLbbutW8+VF61inx/q1AkgQaApTvXTgJNATvGE2Bm2iaNnrJa4SmgUfMz3rKDlOkKx3GwcYJEtWSFQvbHgtunAsgvUh51sQu7TYOjAmM3VZgt6AuB34eOcBVMf7tV00r6xatJknp6VqQFE8wNbXyTLGRsM1wITn3KAw8IMiHPpSvYklS61fIacpEy9oJRQsyoQFMZaKtiXWB3gqdLLGmsJ8FTuKL9NqKIFo/ABCoN51q19ZHiTij2sweFRJ67DiDm2+u3O70qx1tr0z/Zv//kuzhvr+d/6XbXvSZ7zzXkYFtni9a2iZN8ZPE4FF8QD2eO3vCXTSbH2B0Ps1PfGKcsU33QTPee2MweNDxP0xr0ye1AsRZ5tifwFArBIAsiK1RTbiDnYrkgImAAhCAN5lcckmOSitkbFdeelNJIH8fKLDZWFVJOf2v6uywbMWp8FvfuuP9IZPN2WADjDgNIUvVNrDZNbmYAVbWI4Hz5g4eWm8wEa1cmS69NMeR7J21JAcYl2/U3MEHcwdy2g3cqK3ML+bNLk76xez3bqtnCgcoaUvzNkTUEqmtyOKSmiGXy1YWfgp2JgvQ8s1WObS671m+FVE10JggpzKxx9C7NmD7dbKYBlgpHEZePUGdUBXlenrDCZOPtTh1FaT5nrJ7nKnMknoKJH8uj2ZfkqxoIEvzqeEkpy6dnm56abqyP211Xs/Ipg1f++Sm+3pseROM3LrP6hpAfdrqhYq+Dp7gFW1qDLAp2nNOm0LkRZoiJ2ftMO95z3zt0RU4DDrjHiZAS5yPO4Vgo/Ixj/JkOmJDEsAHcID+Rbeyqdi09Y535AFqsIKdZVoURN/LB55rRQZbK0NLq0emdh9kZ/IxH8MCZBIxgLETT4GgIpNatcNyqb+0kYFNQCGcalpBhy7ch3c5za1JhGfBndAkPNRMuGQK+HRpj3Cgi5P2CNu7hXY5kHOAeli5cmTb1lCrhWYtLnJJKpimABbNXKOKxAgXkKJI4P7UIEWRQ1wwGFCxvB4VkCHiyuE0VDiI1jlFeFDhQxT2e9ll+UvlGCGQxvIk+jo4mKtSh14qnc1JTLGIK2X9zd/kK+/JIrOoHjZWmlBUO3R2KGN8K7R7kaDVxdy08cL0uX3ShlVpqQMgtqQ596eDB2xSbPtat0r+NKVgKWBUOHMEoKhJqGpeMIdTU6hMhvBcGysCxcw/VQuSisoDLny43HY7CycVOceFdukvZ0AAQ8wezQTJOTeLy0mfapR+1GonLrJ9ityqsCUeNyQMEtBhV0MlFXP2gbcaF/SpT+UGocniJB3BoCs33MYEjdKEIFgW//lG42Zxn+fRVDWwTXPLHke8+nR+AFVR4e7FruNAFyftOt52c+5yYAIOfOeG7Oqr/n/27gPOsqLKH3h1mJ6cevLADMMAQ87KOCAjGSSIGVddwVXAXcPfuGsAxIC6RnTdNSvK6qIgSUURkTSSQaJkZgaYnHPn+//eV923X7/UrycQhj6f9+m+7766FU7VrfOrc06dSqfNnuqHbm0Eg5cDQKL7Z8m8yGQr4G1HKmdhLUZcMcig2OCYVT1OAghgIBu1MpsR4UdH4tx13sFMeM5zYMHRWIKcPw3LDlEqMRwT1+V2zF1zTSqM+0psKJzfy8laxizmKqQUAAVYqYYs8RuTlj06HhZPEkjyyIDQ4vhhh9OtrmmM0SbL5UNGRmEJLvCRsnWRfSoyk5durICvwgligoKoHPwtR+Qx4yyo0SvRMm4eyuw1Z9HqaVAojWiSSoIkOWgyPQpM4BNRgr/gr8MBozpNgnzSXxCSCNoULRmqyE+wta6Va4Tng6SYM4sYY7rNB1USG7qwlpCQHoz9q9rc+yyHgCREbakLDDOdq+HGNmWw0Ns4Jo0Py5rXzaig9uunFw8H+nHSi6cv+mvy8uKAmXTZko558wiHVMr2pE6oZHkKSQAK9PYubH8rUAaYc024lT08eubc52/wUMnVLSUTL6Vi0QvoMJxZT/MK0kYigZsqdQhTneVygcCDAMhIAejgLf7skIELyiTREIo3mfOdFzmQiiUDZ+pGDtl+qKBydPrpqcWnHNb0ICRqvxUhB0NUyCfmr/76Qo+AdA/eUL8kTGwMj8SfhBWdGW59POwyZkTbirUCc5erUacQlQAOkJUNdzpREFEy0p6yqF0jMtWKOQYzyyE8BQB2/IG4qVFH9Up4VcD/co+oG9BjvyQU7tqocwoNk2jx44AdoKOv+0Ri1gwJ6+uT1uZVDU+urjv2pEFnnFGDD7oebuaxd/vtKXP4hjNFYfg2JZW/9toSJWg4qFQlSWwZAPR4JQGgOJZo+EDDCJtiPiCRD0Ob/pIs9nV+EV55gxw0LBPVNj9tVdcq5pNPFYZTfrL+624OVBFj6aWUpD/O5Eupt17edRXh7Mtftq+aicLf4o+5rcdHQL+jj06efjpZuTJpauqOMXjhhd1BJgseKf4qE0EXxSp04dcsSGBxynhHVMZLLukuq6DHgLyPfjTZbbdk9Ii2hpqNtaF5/PCNH3r36qf+samtpX3+/DSyYjWxEMWgE4lRQMhYqOCQIhzeemsakbKYZPj73ycf+lDyk5+krLjqqkRMzuL6a+YxxyR3312UgefF9OzoEDvxwQeT97wnDajoI1yn4IrF+VS449TUd4Rf/y0cenM4/I/hhOvCUXeFg9/X+OtjjulQepkHO+rDRifYOPp3fFh01KGbfntpu/CMK1akoS8bG0t05axZyXXXpZEeC0g7Fi9OzjmnD+E6ddaaNQXZpKEvDcUlS9Lc/F26NI13+s1vJpMnFzbBzQISHHLSpMJkZRqen6zDIT8CftWENp+BYf3UhqWXXNysJluL1q5NIxwuXJgID9srafhpp+VXr/taWNcqSRBaUUwNXV0vBGv2ilXBje7iYmIxUQVm3CokUO255/bIX6TcF1vIx63S0r5m0h9nshsF9l/1c+BFywF+nY92aiKqqqNFoeNB+LLwhI2niNOmWN+zZFl8WxBXXtNb5tLcWKcy9FjgMthZvtMTuOaBREOTqQoU5Jqhx4Keq4T1ffZTQUXphwTefPe7Om6//Llrfrz0gWXjWzc1zP3DgnsaWxs/uOfUqSPLPpmXEY0IPQpNErtDJHcYL2zOspOfykqF8yvgmkrJJ5ILD9K72EbkwUgUJ1Qsjk3oYS5sb29btHT9cysa1q4cOKzhvrmNZ563wwNPCyzZ6UISzVvlzEadWef9c4zp78Kxo8Oao8JfRoTVLWGYk092eOthp55ag6UlSbIZ4XGeTPy+x4clK29d9JthO/7nD8c0NtbalcZ6VbD0lwm3Hq4zDoFxjkc+HxwdQ1Vmf1bxIyWLdpPmrGDrosZSRwle6uxYticWH14yXJ1Kmuf40ND8GUgZsYTSe/Wdko5Q55N7MGkJg1a3tDz5cNOa1QNGN3a58/Q90/gEfQy7Hl9yI0pVmSMpZmK4rHJZUumVDANLjWonaZWED9FNUHfkK5CqfDw/mfe6QG2c/2v114x9+GDXaj4xWxsGfMj6qXoO5A356h/qT9nPgX4ObDEHGCyWLnQcq3m1D6+huY83Dw9TqIiYZK8h29hEmCrsPovbsIurBjfYkm0PPNBD26/IKHFdmN95ntp3FqGSO7xrefvy1I6hdxRRgeQ2buDK2y5b+sdF+9gBTaV//fJRj353VXPj6jf968DByaZU5Nqoky9de2aHD/xei0GeSJ6ADsDBasZnPB8i5GcAz0GKtiZBEpyc5EPGCM0HW/QASS0tD107f/HFNzoTeELbM5NqltYkux4T3j0/vCV3jluaJRNJX2ltGP2DmnddmxyxQ1i0Oox4JMwYftmQN4dUUhaT/XF7hkdXhcaBYdPQsKE5DFobht7/l9W33jhi3FsG4kOG8wqexQryXt/loxy77gXp0V9V0rRpJeynnIi//e00BBfAJCvwtALZzsbCy5CakTt9JzXOB0OujZJBd9/ZtnRx++jGPrwLxUWrDzAHXkfSC6JawECwYAXCVRjUbsECFyV2RsbiKskKhKM9Ox02Vt8pxZkb57HOxT/19Y7K8JovpmxBUvxT/52SHNiiQVkyx/6b/Rzo50A1HOB1scOONQ01rS0JG1iBm26+IEkzc6Z9vrs3cMOJwXb9CGJM0OZ02giKAXAnf5oGI8zgtoyZfDl/gDVpbl3ZuyD2+DiTJfQWNAo775wms+Ks4DicZpEjcuieO9uu/O9Njz1S/7bkmtU1Qxd1jF9UM2FBy+irL100q/6Pu6y/L92FZTnPm8km8qzgrhz8J9u4wpQkC3T4gBhz5i7lVgWyt/8b30jbziObLIcdOdZAaDReqTdGe/s9F9//uf/Xut+GlSeFVRvCsKeSYaPCig+Hz28K9d8LZ/SU3BXKKfFTazLwibCbT/yteXkaYLokjQ0r1odhA0PT4NDSFBpUY00YCRt993v17XVpeHeNze+7LBM3ubPQ/+XjJA3sE7CDJrk6FRB/Gnos3QJJ08T0StWMil4zKUhgYLeH+lWr2p0bWPBTX796LzKQlD0L/VhF0DtmGy2zn+KFEcJ/zr5RqwWqTdyGy+0xLNDEFDxV8NWiQv4RJxX8VP1XNaFAFdm8B8Sv/vmeKb0IWXyynr/0f+sbB/pxUt/41Z+6nwM4QH6bfM2n4tmQHEQdhGEtDqmQ0FABr+FSkKAH82jXDz+y7k/XDFiwNMnBIGBIlgBTF4rJJXew7iHhwcfDNIeOOQ9qVRjVHgb6hYTjK82mEEWXifX970+/Csatbm6Sqb7aW87cZl+9STxFDKWI+GRfg7pISrDJ9qtqSF1vuaH5q19oHz3n/rOT384N0/6R7D0wDBmcDK0PQx94oPamR++4q3VdS1K/a8MV+53w12Gf/H+p2qcnycRszmRWjug56M8483JjZxerQLKyl5tEZHZxjSG0YlRoONAx95nPfaJm3oZRHw1/bAt10Almrg4j9wkPvTt8/3vhXaEQp1YoZ/N/GhbW28BHk6QOq8PopWFcW0gPE77vnuSCL7SfeXYdOFvOYAcQF7ii40a5Di2uovBLJaWvHsf/OIR6RV38tSHyfIKu+k6pAqnnU+mA3/ugAY1jC1YLPVNtwTerCIsBm8hoVY0oChVKx3zbFvaKMy5IQdwTAF57tXt9hfNrJM+4qS3/Zl+vcdiOuYKoVH3NpD/9VudALzipo6Pj8RzttNNOe+yxh/N11aCpqemXv/zle97znq1em/4M+znwIueASZaTEGcOUfsIY+tOgXkAEStRe7OpTky4Vpb8NgokSnG7TNME+XPP1f/gB8mzz3RKjhqe1XlCBEg6M1zy6nDDF8In4SfbzoXqmR+mtIU0ViC4liU2p9O42MZsdz3h5ys5CrQBPdVM9yRlZYVNcf1pOP735zXPzHn2I8lPHg87XRVOHhiabfQZG5ZrQVtHzcVNrxVFuT3UjG1ae+w1t//z1KtG2iTWE+xQ+USTX3H+2R3nPDgxl6KLxqW8+S4N32eDGI1CJOKfeobAs49s9Z8e/OuqY48IN44MaxaFiTnNHLeYBha3aeFJCp7mMDQ+xSWF7C/QyWU16dOF/oU88k1pz4Xxe4YnRFcaENpFWmhJ8S6InDS1wXbJ//1v279/qp6FiJWtgOAhQKcgzCA3NSGmFJGNgYKn4ld4kVHJVqz8k3aylGZ0TTZuI1TK7pe8MNQLkJkNcXB5sc205OMVbg5tCIccWjd+QhkgX+HJvJ/AFDGvSxIWiV3EI5BhGtNc0OYaG/kbSPUXrZLP5pEwoRZL+OMTjZibkQ+VYTR/b8azxY9YsIn/5KXopy3kQC846ZZbbjn33HNra2vr6upOPfXUd77znY2NjZs2bfrSl77Uj5O2kPX9j78UOWC5f/75qT+NqRBxhyQn2JRod8y8hDSXEa61JmJmoF6JjsfJTccfX/PDH9Zwj4BvTOhgTSb5Xh3uOTFcZV/VmLByfpgGJ40PyzaEoYvDpB12qClWERF7vJ6fH3rkgda/3RoOS+4YF575djiL8w0UUhfaO0KNj6hC/DTsaRockrVh0GUtx02552+nzpuX6rjyCJop6UKRlyS9ZD4QKYBGhLyH50oiP2dBgG75jkE8fvQFLcLGxU0UOWvDCIqchtC6qUt7RI9Bt9QQ2ptTDUeq1YhH6alS9MktqEafvkIwwEc+TmoNw3RcYzo6Rm0KQxRZy6MrtajWtiW1D93XLuomyFJMxDkEXABlGFLjoTcMl9mAyX+WwBbxnJ8WgAW1l4SYFI1CNDnbpBoSSQgW4QaXET2fc8r0S4FnT5agzEWBSik5/Ojagw+pjUbhMo/0fptGB54uR1gk8IS3lUtWZJfirHaA6a1C1Lc4LOcIJfPHYfX5CxNgcGwtskxiPSwglTSD9VOfONALfv/a17524oknXnbZZeedd95NN930k5/8ZPXq1ZRMS+y16Kd+DhRxYMOG9oOm3vmpqRd9fuoP3j/1sluuXlSU5CV8w0QMBomkR/gRWmYcf6mRiFU3yXJ7Scx0kvGNrWZhSt5Hd2kncwnT96UvRdjUKdIY42aG+4aHVa1hEF0IIEKZ1BwaBoemupoO/haVI+tsa0avXdm6ckn7hLBsXRi8MjSODGuBJPqkncIz+4cHRofVR4WbW8LQutA2NGxsCvV/W7V/86IV+bViphR+qRrBQPwwa371qylaKph7oIQf/Si1p9Al0E5Jmf9hRtEjM07cdUTN+vvDnk+GGWPCCpVUjRFh7fCw+s/htUPDurrcHVbBj388xWEA1paTQaI+BcTW1hLqh4f1OZBke3x0O6NVCi1J3U3XNUNXxlUkwwOMxiLnaRQokySgKXS6C6OkPWgwU7HvEZ7YrmUccszP8iyoD1VoNPSQ6/hWmRg0ufznLwCMf95vSu87aXHnZ/Lk2re8tW7GDF+3iAyDfFRanJeRQFenmRjro3cuvzw93merEO0UZspcHaJWaatku4WZCHBPz+pvRjjABaqf+sSBrjeyzEMPP/zw9773vbFjx77mNa+hSfr0pz9Nt/Rmy4d+6udAEQfMEe8c9utPhqt2D/8YENq4qc459fCJN394t8OfLxVHUZW27g3bqTjPRpBknkVRx16se6BtomFiKrrqqtTpoTLJiusJN5pPfjJVh5i7+TXn1qPJwJz8Jr8ODH+nEbkuHLsqjG4L9dg7bVodi8m2IP0IDoIdVt5OVYshHwuUGcodPbZuwsRk3tNTB4fmISm0GDw9zHtLuHTXYHNRwq4kPvUdYVasYX1o3ThodPPIMUxNSM6W/iwgUE5l2RYfj3/xxPFh7HTxNAn19DiIecEFneq9/MTxGqiCPAbPOvD/HXjFZ+993YXhE58Kn981PAG94eewkQMntK798Kbv/mbHD3/kSxNMbLrykkt6RwzFBVV9p+7psGtDaMo5LzNKpiOI1AYaUqyWdBDeAFDURV10UWoyKwdxlGgPv01wPsjgxAcaKWOPWvHCC1PdW0nCNwpRrvGUo6CD4Qes04nSteRGXcmHOm9SeYoakE8eZy2qnqRXgUiuKbRES+cb1NMeW31+3Slh4mpIoSjWwV97KmnUerWSl8zZ6BKQwsbDadPChz6UntnCNMwK2SsbS+a2LW5ScvN2okjrpy3hQC84adCgQStWrJiSW6rsu+++X/jCF84555yVK1e2Fi+UtqQW/c9uFxyYPHzeN8LVe4R/LA3jqT1GhHWzw02XzB561pMfm7BLdb7BL24+UNQXQBPzbDbpF9edlYkmQITlAg/c4pTZHULRVmS6Imipra3ugTBjdhjUEFqaw8CZ4Y6Z4fZNYejvw1uuHPhGSoJ8R9Qshy28IGVJUJqtSBajHDtoLDhER40FyepAupbW0FE/cPLEDdc+feTbwpXHhGsvD284Lfx65/AUT/OmMHBCWMpRaYewYGEYZzdTUxi687Q1w2akmFEwYn4t5m7ihHSsHid5FrevuCKFbrR3vLZpOIRIkIOuKTknqS25tW59zb/ffEp4+72X3LvPuSv/6+BNc2bUPvb6afcOn7LhDcsfqD16zH98SyiqtMVkLe/4bUy1kFtXEamNkuJQoNFJYeEzYefQkirYACBQ6Z570o2KcUtjV/qy//WOfXblttrpVpgSN3BJFAkoKmMXlipxSzZGUVwBr6jCu5Bfbw2Wkh8YmxePNeZjd7aEDCS8wrFqqKCShpCVzOc+lxooK0DSgpzxE8p04k20k1J2QvDeEbnF2O7McHyh3ETWGFUip9mzC8+cLii3/+sLwoFecNKsWbPuvffeA7r87118/vOfp1UaWiAuXpC69xf6IuPAm8INe4ZHWBYEhlG1tannR+su4amB5v6XOJkW+R5xsbRJzTzra9SvuKhMnL7Z4PiXVE8AEHsK91tI4uaFsw4K980O1/E15vlAjfT3MOPvtfsdPIudosy0niTti5Z2LF0e2nnj1A7YeWq1kjZXRUqFDCTFOmujJsAi8NOzTzX/46alz/x9zTPP1T+6bPTy9QOGDa2/cMN7zwo/BZJ2CnMFB+IfPTSND7SRymTvcP/ccKIgQ/tMXnfcCQNqhwy2BNc6ECeKK5K7r8Ssae83XYuKET/+krLlOkK1P/Wp1OvoVa8a8C//c8gH6pseuXxJ400Pj3vq7hVrGu58fFIYf8DIY94/vbkmgk6gJIrtAmna10r2lh4ugI0iOvC3fVRYJ/JkfEqdqbUgSLNssRqvt5y7f8ccB4rhjKYZuqAMBZ4LCGlLWldcJefSwGcMx9E1R+ZgK0MhxBkL0kFwg3bpu3hHld797q1mAAKSWBj5ukUE1s2C6q7UU0ANUk5EjCrJbGADZoEzmXOambQy/y1LHSAewzW814rpa7s6cJIG96VO4rmvX58h32T06I7p07fVTsbnh1e94KSzzjqLPim/KgcddBAn7uu4R/ZTPwd6cmBIaGoIbbldPJ0/sNRz/uCE0TPhS+ybac7xq7TrNEORzPVxuo+CoUJ7yKQ+WSVkZUpljOCLQ7rfcMPENc3vXbB82vprH2xv7ZjfsPuckUdNPnB3JpXCEMNEK/eoFSvaNrU2/eaqtsuurmlaGwYMHP7Zj1ui1h58YK8i1/KXWGUEKSaNJWX32mXTvef++bC1V706PNYUBt8fXnH7xDct22Ov0QuHXLj2M5/d8XsjH4suSk6isIVrABPhfjUPPRpeMXmv9a993/h93j2LzAb+ttCwRRwKKalKkAReoaw7imvuDhsTxogSZLH+8Y8PmnXWAc/tP+7iLx911SOjH960w5olIyd/qEEoSx6vXODZX5ix4Aku4cVE/AMfseuLf+3jnUyQhPrQzi65KOyQ5RDVD/Qc7CabRyQ0xyZYxAgU64GCiqpjy2vOQlcMJrwFkDQdFau0UTRtWjp0wQ5OezjP9Vt0K25SaiIB3ACJGr3FgY42r6V4Zf8plRiNGmEFwfSJIoLhZWhPRnHTymXFg77ke02l5CXSUo7t8oyZR7YLUcY26tqvxcQjHq7aDsghPGec0SEsSFdbOmbPbvqf/xm49969gI2u9C/G/71U/VUst100b948u94mT54MKqGu2/3/+znQyYH5YZwtRcPDOn9z/hZm/zazIn+alzSPrBrF6LMUzifznc3/cWtxBfFjouQsshnEVOfgDp8QpixedMY1/7eyualj3JDGMxrreM4SM1BCNxEONiNdemnrQ483bWwbmKwfkm44D6F1Q8c5n0mu/n3thV+vsFAlaR54IMWCgg8xipUk1qs//Medn2z6Kn8arkg0hbPD9SMWr/vdyI+t2WvmpmWh/k1va/vmXzvWtmxIBtlvT6s0pmbVAcOfnrbrFZPOO2vQqWlg49UrUuFd7M5VssSSN/GToI0Mr9LOIh8mJxJUlCZxHN7xjrrfPzD1gjumLu+KqqDJupKFy057WIo3tzNPSP0CqBT3EgpSUKBFKFnPKm5GnJS66rSGwctCYSQinULP4W+x/qbXzD3FXYaTWSR13ioERLIjexeKSQB0UAmH+eZDReQDBYk9euyYuhtyioDPKAJzI27TlVuF5K8UxcnQW0NhaWFTJWV1wLE+qTYLxkZWnDCVNLK2AtgQwMU+IwWJyyCWDgWzTzEBstsB3Xlnmy2WDzyQrz2qvfnmgb/6VfsFF7yEpUAfqn7jjTcOGTJEdABoaTvo0f4mbHUOvPW7r7rt3a96dZhj5zOcVBvaWGFuDAccHwY2bvXCnscM2YkKQFIsnNu1CZETko025Yh1jPTdQpo4qeZfPjqmbCYmeD4RjrNyvGuooWRxMEmW2M7z5J47O35xcS1sVSqCJKFiBcwFmMt5OetVLrfkuKYrh4R1z4WpSnGnNdTvGe6/ef5jmyZNaW8fsH7avo3/fHz9lTesXlM/oKOjsX3VmOGtDccfNfqNrw8nHBHrwwt4s71/SBp6C3MPkFS9IIzleiQu5cFBSIgvTga2YgJikvQCLI44IvUyIeCt/tmSbJj3rKKJeV1JEQJvbSWcFEuuBBac2cePm6m3mPSUgD1Ap86nRynoWy5lpPXWItpN+kuC3D5zdqWSKi4sgqLe8IbCMi0k8ok6ymfrEvzKxsdDyOjm8xQxdH4REtBplaOYHpOBuepJQMiSJDcIiXIoHyRJ6b5R53wYaEkkheJK2ti4HdC11ya33148pAGGlzZm6ANOWrVqVXNzs6AA20F39jdhW3DgTWfsePew913zluE7h7nDwoaVYcyNYa/Dv3JM48TCtfK2KH3b5VkuUJsZn+OnOL/lcBLhwZoTtyNtu+qlxgy7boj6EHIIqRskdRba3pY88HB6xlVxNBVuZGtTTRJAUBEkQUYdNvwzt0WQJGcaoyFhw+Cm5WuXtw0dOmCvmUMHzTq7YcqExr/dTqVYO+MVtUcekSoWiMou3RevlOpd2vM5Bh7x3mDLsFGugszLf6TgmmQC0Txru9a9d7ZPT545ONznIJF5Ycc5YSYPKulhjhtvTBESYSamInxgx/sNN6SmHI1QAZIYoHTAy/NDAFnJ6VZPqRUnaKY03c6VjTKM6pGeJhJvm2JJvNl15jHDx7kUxt7sLHt/UMMLjNpMXXoHmjn++B5YDQyyFGHjo0miOStuOBZ5T3PvR2G5MbH3FHout55REyDb9lVAeeedO9V7dLpGcsnlk+HBX6qYJKZssyHOjFHgbm9osVRuF/TSxkPluqAPOKlcFv33+zmQceCgN8wYc+8H0wA/oV2kwcPC4B13HTFEDJ3tlMywHC9scSdIiidoc+L55/eY07cJG7je9KriABDKmKkoSGhZereF1dQ+nuyyc3h0WXB0RbpktEq3kW1l7aSmpOH008ALCp9das86s/a0t6ZCiSGEAO9CSLHhPDNKiqtitniUjQZLcRheocy3RueRTYlSZQ7FeXoQVhTOcdq8O98WfrZreLwuZcqg14W9Px/+Y2HY0SP4RMixDfFVoqQhlU8+OcVPhDFYDK4xbz5vOAkgKLnZDV7k40WZRH0CepK+9gCqHotY3LFV0gOmmCH5d+BCm7O0tIDwX6DOfHRL3tsFKUIjnjC3iWdRYGAALHQclEODwqvPC0I/V+XGe8+y39G4qIlCMf9tb0vhIG3p7beneAVDnChyxhkpLlS3SCAOvygudHzRigmsFMSV27XMM5K5+0qRoZ8ogSZOzH7svhBtyN5MXlb8nyBF48GSxLN0yRVemTKvWtpfBrZnFapFcuDxZnsE9+0CHnbX4CV2FRdpXR3zEqt82er2ASedeeaZgieJFFA2s/4fXvYcqK2r2flAcnS7IqLRTFqOSAtiwNz9zW+m03FES4SW4JOkiIttTnkgyfwUJ6pcROwGaJUTvaDQNVMmpXqSUkRa+BA5xTgvP7kDVS5N3nxouHlKWLA8ONU9jb59e5g99tjd/um9dcceFyV0bSpMymseYCfykgTNl1j5pWTXYAqWiuEkM+Yekgnioi9R1c0mDeSru3NY+K6a3+4eHlwXRrSHIdytXh3++r7QeF44H/6ThvTKqsfGVGBmgtXASpbKjIg6kyIXqMoMzNKXvMCZvG5Mk9DEgYYlp1sVAPgYwiIKhY1gF47bjqxhCEYwBP1H9fXRKcYwNOBTMBJ4a9GjZFIcqqasEvqcuRl0iEBNB4kcIZ4FlCmcVdyiDyvALjCcSgos5IQ1kYqyfNJa9iTP4qHDaOWfsYIx1CYy+cT4kJ6g/oSWmE1ZvpQYCfdOOy3VCcVIjz0zTr/RC4pJxrbFfwi74BL+WxG6aa8uxkwXBaSUE07oPBk3/kSnJQeJIUVUwKuCx4u/Ui3T/J1+ejq2cUxn+eq9xMbtgz784dpHH01MmD0p4tOX8mo5qUiXX355i0OcE9v81sdI3K43btz4jW98o+JzL9iP6jZz5sy5c+e+YDXoL3i740BzczJoEPjR4/Ob3yTcgV4U9PvfJ3V1WeXMSTaVLw1jF4Xxi8PY1WG4v23vfm/S0VGytsuWJeeemzQ0ZBlUuhAQ67fhjfeEA28NM78VPvb1f3104cLS2ZYsy81LLkl23LFSEZHP06cnF1zQmQc+P/NMcuGFyeTJvT9Y0E3FX2eHO24Mr7khHHF1ODl+bgyH/yUct2Owwy3N/13vSubNK1f9zvuzZqUpncV3wgnJ/Plp3QoY6KfioivcUeLvfmcHdfpUbW1yyinJ/feXrcOPf5wcemha9Mknd36OPDJ573uTxx/vfmTgwGoroKqKe/rp5LTTOktXgVh/w+ryy5P29s5sW1sTY+3gg5NJkwozl37KlGRIehZL6c/YscnPftZdvfyrjRuTX/4y2Wuv0g+WzFBxH/lIjxFtdG/YkOy7b4lMvLybQeTe2WeXyC2/PvgTGZV/s9fr+vpk2rRkxoxkjz2S178+efLJzahd2nbzkia78FmwIPnpT5MPfCA577zkjjs2J8Ot9cyZZ65hwc6FvTAV+bS/850vkomyRxPXrl3rvNp169b1uFvmSy/6pLPPPvvRRx8Vifv3v/+983A//OEPDx8+HBZxjMlHS24g7gkj+7/1c2A74IAFsQ01VoFsDYg6xL50a/cKi+PqWx2X0Qw9FtPchKP2QonWrNXmzzyjNkJ15BQITWEAW1JXLIYOfkXLwqT7ntn12Kfn1u4yvbhiDAEWzWxJXF561dbcHGbfFmZODc+I/jB95qQv//MA0Z76RMrCTDoDu8zKmYdoaPgLO79WgzgVsVCwxVQwc/SlAsng0Cbsn7id2VMcrcQ9F8sg3jlkv+Ydh24My0Q2GJB2QzRlZalzFwVe0tycqTd+8YvOED4sRAZJr8yMWdJqCFhFVcYFioamJMkqmnIMCdf0N/RM7vhKpcGeaBTpiPyzTViU2A2jHc0j9DolNV4e57rOtMQZnPaOPon/lvTuU3LoBQ7OBqfiKPaUYkcbb61iZ3w9Rc1Tgehm5KwOOJpP8hRMS19TiVVPijMejB9vir92n33hC53WuoJM1Hzz9tvz2LP9szJh1GaQ/vKJxxIzoVKYGd69vux0nJ6KXcMNi84mBuehdxSrk7aMmyKSjAv5+9+f9h09HEUslTadtwEZx4kExrU7unhb0A9/OIJp8uijm59+GrpIDjmk9eKLNze4xbao32bl2QtOat+8gbBZVel/qJ8DL1oOkBZi6mx1IhdNanYnmePMYmY00sgsxt+aB4ZoLqLz9U4EuZnJ0Q/33mv9uyk0sKEND2tBJWGeAJp7wsxbnp19wCPLJpTCSSZQthUyUuRGkoz44RKbQ1ylS24NA58Ku/lt2f2paCwXFi/mUDwX88yw654Bgu2GCcOMDzDl7z7TfG0XrM8UT6xycOFHjzl9JflwbCLjZZ5HNYvC6MVhh8nhuaYwiK8w4OEousfCvgsCxJfs1rBs+o1/qLv+0rRt06alfmeiTat0RSJ1nHkSz3PiVsXJl52oGgJuoIRyPIw5gDjMfEYIYMSHxld7u1imDB5fNdORMS5YnUDefOLAFIm8NHoZhQF9diuIxOMQhsHGhY5rTqwAoGY0Or4tOoEZ8xILDS/xm96UWs0iP0virfxyy13zwdcXBTgJ5MKBPoGkmL9RD1sYZvA9mAVwZGTUeXHwRBOcXnfssdkvfbiAPMrh+D7kUiYpxuKqYaXL+FTxQM988Es+AU0ydDKtAqme4kmmK1kbvbx8uRxRZwQaS1ZZGCIN1MhpUv3lDzCJG+4ntkIw1wvOpYy9z+g2eLYRXX99tg7pBWNsowps3Wy3hzZsXY7059bPgeeBA1Z1RCloQlmSCXKSL2oUCEJg5fzz043WJT1UCmtoPcPNwTzq2LWwIWctqGkLtbyUVqWmt5Frl3SseHTphCOKlvO5jMibuCqlflAZkpJYVYfKZGouULUkHcm6BWtWPTyvpbVuWce4jR2Dhg9t22GXYTvu0sOpkYQmmKNshkYsi63d+dwQlmQbCMXRFUgy41NvWG1vBkhSc+t1e9NIFM4xEbTF5jwZJl8fTnhTuIR/ldAGzoRZE0b/JrylKQzjO/7hgT876m8XhpaccgNe4IpM20MOFyO+MtwBdsWG1q0kmXJJMiIwBl0sVsPQrkWPojKZpTz5+c/T02fBL10TMyTecN7HhViUfLlgucxZpzgrkpUvNkikQYCjgRfdfUhKusio2IhPqWo85pZ7Ew8eMjgSjCXIKoWZoNtk/OYRdVcx7tcuPO4raRFtmXELYXz3uz1AkqwMMO3l1W69UQDLqi8Ib41wAGVbkH40AyBNiGqecqV4s6X0ZhvGXgcP4pgPrGOk+epDQQsG0f+5VmcIzPJD/ek4oWrBCCBsDRFmAsZFGE7lyUHK4Kl6XJer4Mvifi84iT5p/vz5PJOWLl3qWLd58+Y5sUSAAFa8lwV7+hvZz4FtwwH+sKS4gALl3iR7fKyzwRcTYu/0/e8ntol3EYU6ldKGMHR1aHSGjIhWw9duGvXrZ8LEdWlAHvNrqdnRPbMtCWTrEzkacVJMWLKStP0EbUbSLH9s2cJv/O8DFz16a/srHgx70fqPHJS85vVDTv/Y+BmvSA/4a121fsMzKwaOGTZo0qgaIkIMzSmB9si+aLiQADazk23qgEzupniL480g1Wacok6AQjQq32a3MYy8PJw4esrwg8NdGxesWdwxYU541R/C0fDoW6c/ecqi3wx0gjOZIwtNInMcLWvbFWVL1SRqpYYIx6xcclrcReBP8AXII78m2k6c+1uB4BJG3nzMSmrSEwBJKXzsSF5zSNPhR9TudcDA2FPlssJscMqnGtIRbFUZSIqPYIae2mwDA4REKht6BaTaVa0Eej7m1F59gihjojU8a756wgR63/jsK0iSlQz1UTqYlwfDGzQpJtg3opzin6q8QxVkWGImVtPSMbgXEwhIIwjXKsuWVtcaBf2AzjSIsJGxqZ6UhS583PTiWGBYe3hEJRVhIANVQqNplGvrEIzyZsHBlhDWVn1lUXE9Xw53esFJGzZscPbtgAEDnnnmGR5P/tbX1/PsRi8H7vS3sZ8D24IDJByrUwWQZPoznYEO8EpVOIl1Kl27AEi5MNy5SteHpqFhzdiw2N60nZJHB97dtvELCwfc93D9G06ugb9MokVkqrXhXGaZeJBpOfJT/q8d7e2PfvZ/R13680fCP98QZm/MHfW6pilcfsn6hrUPfuLnB979/VsfWDB+2ZObRk2o22f35ln/ss+wyaO004QOzRSrVXAJUtw80jhHCWAdcwwhxLSXTx1jJzT+66k77nPYmqUb1iwcNWPtmJmja+CVg1eunfL5eamEiVLXXzKcwAR5+oKTZHDKKSki1QSAJgcIw7velQo5x9EQxhKonjg6dkRWJn5jEHMBkYJ6SviexU/XX/KzlofvTl45c+2Rxw+esntP35+Cx6r+CpuWDAm22SBJw23vp7uKfM2vCMltMxqFWTnCq4KRJhN6rwi5iP+SKh9pissqV0S8r5OZq7yYMUOoAs42hIDU/HEuccHXytlqu2FQ8Ig7EJIagj7OllFWAbGy2TBre6ZkhC39kPZCSOpGJwQo003CRr7KChljLnScjwtfrcTo1XCPSkkm9Ew0cJEnquSrl0KafpxUwPmSX3vBSVcBoqVIjIBSt7fhPTqtq6+++p577uGjfuKJJzpQpT9CwTZkd3/W25IDprleIwGaWE12prmqKJrreiZ15PzwrhOIB4U1fHM3PP7kuguXDbnr/mEnHVlz1pmpf0pPsu0ZSKLDUMNeiS2AsUx0HHMumvdkx6jLfr4hjPljOGZxmMjk57BgWo+1YfgNf1rTetaT11858alkgqhaI8O6XesWvvMfc9750xMGDC7rIoED0UxQsibi8vGsh+rY7ArIrrG3v27j6UevGtYxfPzYEZMm1RAz2coOS3kun/LGAbvvPgngODQnaOEqiKj25rq0Mfk6H9AA0ik2FxUUWeqr7vPJiHDiK0PuklvEFfTmurIqhZjUOpUsprqOTbqouSW568H6ux5MrrmybcHja977iZpxOw0tTtzXO1Ew9/Wpcukx3KZ9ruL52scsMTlNoVKO8Id0BwjEQcj4YGDw1gJoxDKgoxILgB+3mxEEyAqrASk6m2rIg3qE8ox9U56+RjIKZEvTSda5SUFIVWPpYsipTJas1yL4BvH6NwLziY6H/74xzDWQbqyAvH0M33zFDL3opQfoeBecKAcDqVgGWF1gi4arD+gcrzHcgzoRwnPHI36VBlvEI/CIfOSGRdHNv6D0/q/FHOgFJ0Ekxc88/3eampquuOIKQQpOPvnkxYsXf/WrX/3iF794wAEHiOf0/Femv8R+DmwhB6wCMx/bClmZRsmAqsicl+pMzPGpSik+4ioj1/XBVq+NG9qHLbrl6Z1XLBpQV5u65mZk7rz44o4rF+9174G3trzJIbNdv8TcYs7udedqvuaZa3aOOOm+O1qPSJ79W9hrbphmQWsfEusEX+n2UHtfx15/vyIRryg3n9c4/m9p+9glv16401HzjjqzrJAEMqhbOFUUEzOWvUga7YITKy92IoFsEDfoxGNaZzx13cQH/jT4m8se3tB429qjFj59zOiO9hED1i7qmNBSN5ioYO2KjDV/kNO6Q+wrapvdWya8a+zBOz711xTgyI6EIUkkLRXHvLhWvd5RHLlYJcGg3/lOilmLqSa06wamFc5VftU3izcOuvRXTQfOXHvCP/dAB4QiJQQibvNBW3Ge2R0S1ParTBJn9zf7AgalLGTiKUkwSnZebHECvIczIPICpZruZg4FNLnMU93JAfKI2AUgYJLju11Ne3U9eMTIaAcDTUwB+gEy9IIKcHXSCiOCExhwBjk54kbigvTF9feGGUHUNgWEvVzKbBIo2S9AlSarv8EZ4RHQYzB6yrumVpH8FK9VI5OErnEgJoa3XPsJQnLH2+pxjGKqk5XHeXmL4FoBpxZU+2X7tRecdEfJ1zTHLWGKnjeuid502WWXnXLKKaeffnpbW9vHP/5xmGmPPfZw3tzzVof+gvo5sOUc4G1gcjStl1IA9cgeCLCWLbZG9UiUffnc55LTT0+DJHaBpOyX/AuzpXVmbdI2b92Y3aiK6TeiZwRX6nPPtR/moJba3cKofw4/flf4/tPpprYIkuSRwaMMMKVTsCW7KTjS2rWJvWPPhcm5Y49tmPa1gyMsfRJHJWI9J9r9ST+8ph5Ppn7sP5b88ZTUZ6IksRoQMzQHZvaM4v4gfhgu0M47p9YrYizKdcJs9RVz/nb5vX9dNHn1ykkbOhoWh00Dw6JnwrQlYThpUtfeMWxYzY471mTVph4455xUc0CQ1Ifd/hHOOqd2wYz2R1h70hbqBlsB+2KcILocCMjrmWyDyYRPAeY2g4RY1C2xXQWPD0hDGNTqTbjTTznGdsxfNfjCb4cnViWnva2GwQgxnjqrjvIDYRTvcmK+V/RgiLL4bEUySVfwwcJ2ypIISgtgBy2eQNsGaYaB8mvlDBP+yJQxgjJoL28/XuoGJCseHacTZ2WrXD5hMinZah3Ek4zfGMtqOQKeaFj33TeN+YloYH0UaljSdQEiuhsQsVooqLzECjUAODwpqJggpCJ9bmcq0MoiBE+ASIjQV5n7KMV1xEbGZn6Jxok7GXlWykj4b7RDhFEt56mInOAkHW2AC79Z7DeWZdV/kXKgTFylztuC8BYQFQ5yFG7lB7fur/PmzTvooIPuvvvumO1///d/v/GNb4zu5O5wNhfSaU2OaJuknNsfZ3LrdkB/blvGAZES77ormTkz4phq/4rHeOWVVUWzbJq/eF6Y2hzqwBOrzQqfNWGIlNcPOSnZfffOUIYC6IozmMNEzaFeXMrmMODGcFyNg247c+rO1Uo1dzNNLuqj0IgZfefsh5aHsZ8P5zeGZY7LHR5WjQ4rHAAn0Jyn6kNz7llZxY86pjeVXJKEzlOvK65Ivv715BWvSAMYsqaJALl0acnk6U3hEK+8om2P2kemhrljwxL2s0FhQ0NagVjnyJX2mpqOd787efbZ9BGBGadOLeyOHcP8qw79esu73pN85zuC6qbpkkQ0zu9+N3nd65JPfzoNeql6JUkdDjqoR4biPQqtuRn08Y/3yCfXP513GsK6hrBxRFgzMqyMn+Fh9eiw3CfrnaFDk8bGNL1AiDEWokiPXTNoper853+m6cVCrCaCIjmQVSz/OrvpwrDHvXJkU9CXvpT2L0YpNHvQ6PrCFzoDfn7sY933swQuPvGJBDrPSKcsWZL8+7/3SKxWP/lJlqTHhcCMI0cmAwb0SJ+ff7yW4Ctf6fGgATBuXFphYxKTVd5fFS7g2GGHpSEftaJkzM8f/rBHnvlfVq9ONPnAA9NsPetvQRTTWDHFiQiaXccLd7AxKzF2pTtZ78RXSf3Hj0+GDxffKHnggfzCXy7XWzPOJOyRwUm+2ytWrLjyyisvvvjiyVDo80i22iltRJeXwMiRI1VMf8YqwEbMcCJh+urmAurFfurnwIuAAxZ51rg2ol16abrBpOSyskI1OXFbHNuMzb2DcqUctbd13LD7v/5v+OJx4do9wiOTw6IJqe9259tR8JSwk2vDyI6NTZ17xP3MZbTLx2dAaLdeXR8G7xoePT785U/h+NzjcaEqw+4Li1eOyaKwZDRs7ECZTw/zdggLloQJNEYsbh0p0koaQjPd0qacZ3dXJoFVjgaHXwjvDVvWM/Jac76Opg0MtOwWwsc2e1vry3nzWNaLdsNa9LurawaGncSOag5pfPH2MIDzRlZirohU6fbHPyYnnVTD44TxrsBxRJrnwtTX3/axv309dbXWTHWgPKDV0JXo6qvDhRemR4mJa6BTqLXiOl62LJ8f+EDniRa5stI/1vHnn59uNKN/z4wj2a/lLuhC8rVoBclaA68uo0lQg1RLl2tp3ciwlk4hayzlgQ+K06RKsv5oL/sXbVz0CPYrQwwdTL66JW429JSb2h4fL6iArzL8zGdSzY1GYQJu4A83DYM2e8SEbcMAmyZv5UgqTVNCI0LJoTddqACrJvsay5oSZetx2dLi8EySoEIdpIzKFRdGMZ8/RliWsnzyuA3wgosVK29s+NI7Bk8FUh9FZLqZmJKa6qKL0iNWiBq/TpuW9ru2CwzGpUzHaZ2GC0wlBJoLVpmcdOouRwOj5bf7Vt4VnvhQBEabaTZvYLXiIovykqeXGc8l8NHqSO7T2Hkkf+xFZWp8hMLMxjq7FLxokvVTSQ70YncTfdtjra2tsMhtt9126aWXQkvnnHPOm2NItZJZboOb1FdyzYJeMr3ZdpeVM3HixG9961tfd7CQExs2bjxBTJLnhYwz49hbYVCaa7yHBnc/9XMgcsBsxeuTrzHN+ZaQHLjp5Ae5KcjtmVufndc07rBwx2HhNjvaV4XRdAy8bgqS+WqOFU7p4bDn4WFOKh+cWs4HyvA1leaIkBoWNjrxE8QZ7viTHpROonbVEJEdtQ1k20EHtNe0Jx3tda1tNeb0uW1TH0rBzL1HhFvnhFmbwgD1YIN5AABAAElEQVSz+rIwnpp/fFi2MjQSu0Jcd2Xp1Lm2jlBPaoosnI+TyB6gJDP4W6mxDjB8kcolt7V7DW0KE5dSvEGIAS5TFsExILRyYGUVyaBDV9HpXmvbhdjaGEAziZL96kKe5DRhZv5jlBE+KYKkmEadxV1Eqs01aq+9UgEjw89+NgWcUfzElPGvDW6aQC6W3P6dnzK7ZqwpeeZ8TADDEN8sg4KIRtEpmDjJuDKUDUsaa8XZy9Fm6Bc/appzS9Je2zD7yDp41xb6DINy9/nc59Lm5EvWrGLZhSZrvhiGGWEXNCD8kn3pSJcBuzBuzMfrwAUOLHZkL5YKDMabHvPZzoQbBc6MwdgXbED/8R8B+PDi6COPQzNEewGggUUEXIhrZ5LeI4IoqnYx6S9u0cVHNKpSr0TymNUjdsxPrOFgNHSuzsxwRiaG6GVYGZNZOTNjH0kFMCHjU02wBXJi6hJCvyTpKTkAfFotT1+zEVVQ4fyfirPCn8waKKVnI/B1U5V88JPpDZhmo9RNHJX4e8WU/kbMWpzty/NON9oo2f6Ojo6FCxfedddd1157rR1nxx577Kmnnjp+/HjGuJLpt9FNB6c0NDSoCZ8kRQBtEyZMiODJV3ZAm+Bi0SqWD6G2UX1kaySZOk2LXhWjzRtr1ysbdpfOa9uV3J/zS4MDVoEE5xaCpDjSTJp8LXOLhRJtn3vl351nPzvM2RQGOY5jdFhlz3hDaKMckjq+qCQC9cPCsNOdYWZDaGoMS9I5WJhnkIFWBBygwcgR6Ts0rP+/cOZVISqTuktkidul5rnR0xpHbXji+B3X1N608eaHB0/cb+xzm8Z+8DONCxbU31XzmXOSC/4p/C+d1v1h/6Vh9JSwjHZqTRjmhAkghn+399Xb41QPcb3VjvyLsi3KUdod4I0Taz551+jkvGJnnJFqJgrmHvM+CQSS5qRsjYI8qywipiuT3LeuL/4ri/eP8MS86WVekgAgLlsYQ/bbBliSaMIoEqgWiEl9xNOlXG4aRZFjB3g18wPRBfnxLK5AzanOYi0UmHMCax8cNq0JI5rL46SYFQRzzkc2tt79+C5tj78qtDwbdvrTkzOefnLcl75SS3hHxoIp9CKCNonQU645cmNRsDgsILvPuEWXJHoLMRHs/4+YXJcBHwQzNRIvnPyCfKUaNKn++tfOPlOrGtoOq1AwK5+oanzUmbGBeg+26BrC+ak6rwveHQPAqLPjrDIW9LCKUQQWx/BULi0aF+8CgpD4SxUQtSvRIGIqZZu2WAZEB7KYTGUg6Xnz0gHsXaSsgpJ9hajgKmhGgnz+5F8XFJR9LUijtoCRv4rwE0b5Gq+9gGCrXvMuYCNmRg0rbau2qEM/4UAvOMlW/BtuuGHZsmWzZs067rjjdt111wydPJ/sGzZs2N577/2Xv/xll112YVaE244++ugMGz2fNcnK8tIaW8J/IS+b8KZeaUMQWip4J7NH+i9eVhyAkzYj0HBJFrE6We2VG1cjDt5jRvj+oHRSHQrlDE6tBLQDg+rDBiApYgQ+yRvD8GcDZ5xkeniacE0HK13NQw+17bRr6zGnNFz+q9RzOUfLwrgvhw+1hB6bJMyxh4d7pg1fPGXC8DV3P7vu8ueWhKXg1f1hh8cGHNDSNrumbuLN4dXntn3+mPC3gWH92LC8oab5jeNuWb685uaOw54KOy0P45aFRuouGiZnTOWWNOliHYDAK+YSvs9WHUBSgeZApUzf55+fTuiW5vGY94xRlitAUpeMdPxsLV0LmEQlpkWRA1nirosaWg1io4IYIM7ViszA+QqktldemQYHgmxy3gGl02oRN2r8ttCjfiCBKkhohQJJOdhXOrfcXR06UvfWhI28ytaEwhAPJZ9sb2medPs1bw0Xjw3LNoTBc8Ou17cfe9MNR//oR6OhAYr4ePYF13jSWl8Af0R7MWEdjRqUUyVhJv6wi0WQFJ/CDR1XINH95A5tIhzz3HOc89K0lDR5FJF/bogPSjuIdIfAugZAXsKuS5ggKiyxVDKgBCZgS7VM0MUVyIO2zr33vWmXZSQTD5r/DR69WWzOA3MZGbyBEGf2zsrqE5/I8ui+gIGMH6CcEVDDoRY79bzJ7scBUACSup/s7Uom+RTz0XGRMNa1SmqFoUh3RYSxkELSFLp+gufo/AyJnEkpP6eX43UvOOkjH/kI36DXvOY1PJPszM/n0CcdLvB8EZz09re//ac//ekFF1wg9OUOO+xw/PHH0zA9X+WXKMdE5iU3zuKiypg2S5pZvFHVa9dL5Nt/azviQL5U2JJmGV2wT7nxvvtJu7UNbKprTveX8QGis+FJPSg009tkBDHANHuH+4eGtUPCxs6fOL7edtszU4/404iP7Tm0/ZCNVw9KNvFx+b+69yxutwktzqkkMUdsZqy2I8Kf72w58onbBy4Ph3E5Gh+Wzw63iWq9rnX4MaHtN7Vvb2odcEM49MZwyODUezpMC0vel1x1SHLbK8MtC8KMB8IB10164zODGp+an5ZPcvjQEpmOmVdiiD+zeTky7zvozdkXdpB5MBJlEqVOT3GbQiW/5tCYv1rRJRw6H+r8SixF/4/O2z3/xR1A9FjEXgWSg8rH2aBCMj+pP/2ibMkeG/QsqLJWFDwYZWQUacUwomdiOweLtDo9U+R/2y888vbw85FhxaNhj9vC4QvD5CVh3Nr2GicMcl1SK/uzIFGaHo4VxCcRXkwmPfEXYKlyEx2O6RSKIjKYcYoqFDqhvcskt6bFdpVrnfvz53OVL+i4HnWBpeJ+QA4XFRCqsrDdvlFvEChA7WT1AiRVfjcj821UtHUx2lVj2Xy8oHmKQ2ACQpJAA7U36t5gC/pOBlPoDbygooPPCmB9jzaEFHKJLJUdTgdPy1zp2acciwryqfxVJgaVDvUB3Qy8OKqtUmJLXbjDbrjzzp1BL2FTAJSxmCk9Q3uVS9mOf+2ab8o08Q1veENuf1stvx9UJtU2v82Uduihh44dO3bJkiUUWrRKO9raG3t4mxdeugDLHQMr0zwbSYaaRQalcbnpo3RG/XdfyhwwBowEGnj2FDOvCSibUywNgQCL1y0ncxw7RbGSP+Y8fFTd1J+d0/SOs8cmy4Um4ofAybfAj5vAoWgfGVY7/R1gSlVH7DYTJzY9sfDOW9t+9fj+u+13wW3t72lob2lPan81b99kJQNZSkBSzjOknnrmz+Hkx5rsquNE3KoImOzaMGh6urlsuX1t9UlrS5LOJwmvozDSgycnl0xeds/48NzE8Oze4aEDwu0jkyH3HzZ1z32HR0sWGcM5hhziHM2DpAJIytUlFa5OueKWmyEM1hzCj56pJ0X5mu7F2mwi5LzdBGpl0su63lvPgFkN0TSIRGCiMEuUi8pEqQOvWJZGjUI12VaZ5pBw79iwdH6Y8tvwln+EGeI1rApjIqwkoRkQNYeAh4HEFGArxN4Csg78ylfSgT1tWsEvnV/ZjJzAyp0ZfBSmARvtRfChsYhk2q5G9veaBicZB2Vb1PvdFVMWLc77359yku6f3xWPtGJtZfcDXVfwhPfDg3RmNGpwHkuZ19ymCkoXmjAjUJ7wMSipDiYBFdan0rgAJc0GkJxxjqVqiBVcmortd3jVU1uW1kAOPiqvGluLZIistcgpqwvkDuOmqqpb/OovHB8LVW1eX+rv9YwJ0kQvV+oFJ32asvjFQaJv78f6/aIhiwkjKZvZXXhtTDEZcnrR1LS/ItuEAyZH6g0aRDLPlEpYmnHIDzEMecWaRk1JFqMsMpVXrtVUziqW0HLsmqmzJE1842vW/+pby/7tS6NXPVOX7ucvkc6t+tAxKSxwyHrtxElhj93N95sGj3lifkN7R+2yMdOX10yPmW9al/p7pzN1aBMuUlhtotQ2+3vDXrx/xDYclHoND7BvTkGPhBm7hMcEm0zdppXQRfuHh08JV4wMy2w9G5xzKh8TFr9qyWUTxh24x6eOaBGEMqRChWix/vr73e1DWtfVhWZlEt4dYVDJJngk+gi78Mbx/yW02Iay17Cr8HL/O3LhCbhJcZbqBILlkhKoIkxxPCI2KuQvsg7lCu0CcGMkVEMYS4IK/wNzlOxQ0wizF3m21VemOVVi+yNhz3+E3YHddbmYUvl1hiEEt4STCH5u0cVEjvJQLkdWBRzqQYfIMco2HpwUS54CCi0hvAtRYJfLIe9+iTGc92t62esihC6H37q52npGLK4KzoJ6WcVi3Vx7kNqS/xOTN22lx92ZNi0Fr/CZ913HecfdB5XACHc0mVMR7wsYV2Jf8dAZvbAI6562WwxgLIe2fGIBLDm61EERWxcoyw1C0sVZtoqmZtNqF8QZPaJyI8X1HkSYJc6qrTmQX5z0svTZr9vlRfe8VrJ5VDjZfdvNRDB68sknBSiK/tTZTy/DC9pI+mTzHQ4RHcaToeam6bKftksOWF05bswEYV0ICdlfw1phlsnIlMHQQKtv37gZ1kRjq7CTXPk3ZPYdaUpOi1kmJS9Mbdw7TFiZHqUgWe3AAcPf+NohB+y56bo56+5/Orn+ijDv4YI0vpI8jl37bf0Zp+40b+x6kReHh2OPqnmsEDGopIXvnDkd9tA7ZsRzwkUODU1rQgNdQA421eSCVQ/gEcV5fFUYaZ/dRlApj/YJj0wKcx3YauHKZpdbHicTkvm//Z+nnZvyoW/vNGHvCRElNG1s37f2wbeFey4PJ/OsagiJ2AQ5d+8SkjLOy1jBEdt2Kmv9Kvk5MKxrC4PacpXUitrQQvGWbuArQzqa4mrGjBTMwaklpTsdg1140kDJcACRWTJZcQnkq+EENBBOxUR7wRCz1UGSgh4Me3IAXxLGM87CSXnbD7trAXzw46bqK8lYtSL4jXOksQxMqqofuf3iBisSt/eCBzmQsSVZcducL0BGlSzqrtAWXFm0qJjqcWCqAJIksKWUjUxfE/9w1VFHpQjeU8CNxhpv/Of0C5BkBPqqFT4R9mlvHJaxpjSLEBUwAScZRTL32kosMgjYJGcamoyg4ZIkPfiloPwZpmTKPt2UbVSnGXjyNw4VATyxS+oddkwtipNM1DlZ+OXX0OLQe0enCxGa3wySL32pO+hDn2ryEktcOaoU/6Sv5GJs2Yr/gx/8YNy4cSIFDB069Kabbqr84Av1K+OgQOFzn5c4kw8+mAa+23vvZI89EiHFvv3tZN26F6rd/eVuQw4IHnj55cmIEZ1x5MSdO+64NEQbSVHwEc9t0CCRsbPwhGnww+uuS/bbLw1nJ1ici4JgdAU5VPh6223VtZHX0XveA2iU/LSEmnlhyi/COz4y8ofJZZe1tnRcc00aa+41r0lOPjn9zJ6dHHBAcvOfN175vt8fHa7jyzI5PDs73DA9PMW3qS60+IwMq8aHxf5ODAu/Hj752XDez95/x46T6JOyMtvfGX7tIJBlYfSm0MC3an0YIoLlc2HHfws/t2/ugtfdtXZR59vStnDxzYd95pBwh2CJI8IqQQ1oUuiWSrJCsEShHwXiE3yvZIKSN+tTlywuu6pnD2D+p/dMBO4TlE/fidSncz/5yTS84fHHp/EDdS6aNy+NCijkYMmiS96UmwjqOqok3XxzstdefcitZBElb4pO+fXwia+EfxdLkzE0xwo86UNZRq8onaLX6QVxGo84ItEjOON+hYF94olpeuEZP/jB9D2qrsRsLBVcVPl4ItLmtdd2hmmdNKnSU1IKZ1pAZIj3wuvgZZ8xI6223i9Zcw0X11HIyhjL0dd9901mzeoMm2n86G6fGOzR4MmnW29NHyzOViZmGCVW4GrxUxXu5OfjesqUZOedUxbFoJTnn58QZ158lREwk1DzOfvsZP787soa7ddfnwh+q7Hq5lmNElW1tbU7zUvoqk9xJuHcSnTwwQfPmTNHikceeeQd73jHRRddtGnTpi9+8YvcqCs99sL9tk1xkoGyYUOyfn33yGhuTiOuGj1eqnKz3gvHjP6Stw4HFizonCLNg6aV/BmneGLyq3nks59NYxALw33jjWnE5+yROJMWP1XNncMPryo2d9rmyy9vnz69QLz4CiCsDwPvC/s+GPa+Opx0/W9XSLt4cfK1ryUyP/jgdNZz8V//lbQ/NZesfjpM/7fww0PDLSeF3x0Q/g7EiHBdG1p5OLkGm44Kf/2oVfcb5mxc1wZynbT7IzWhqSa0QEJTwtw/hzfyfxAlnKqJzmltGHpLOOZVwYllHaPDsrv+sqqjvUOTVv3h5vMGfHXfcP+M8KhsRfEGzoaE9XbhFLPF5E7cXn11CdFSnDi7k8ZByMEjlsT48XVIWK2qWZoKF9jyrW+l8bjvvTdpaekcVN73pqb0xSdOCIwKjxf/ZGX117925pP/zwyzcGE6eIofKb4Th2I2tPITFN8kcc3ZYM1+09aDv9x2nWCTi5BuXFRVXJYMavzxj9OVA2ztOrtf7kJlTjute+hi4O23J4ce2v1SlHswD3ZnY7n34mJu3sEPfEDM4U7ukv3lS0mRgeYUkOWNkPVHH53GEwfK4WCffMbmXxdnHrGj+5L5+Gql5OKd70zfOEDf5BDpn/6pEIHF9MV5bskdcE0FsvkHfwAd7QKMIF2v1T77pHAQoIxBxt/xjuThh3sINbHO3/ve9CnpxfL28axxddFFBZx7aXztE07qxe5mm9sUpnIxap97bsCAAfvuuy8/obe85S0/tJHxZUZcNdmSaSYpJFlVbKOlmGVxc91P2xMHKKJtn4k7aGjg6eGd/xW137TWtOiVSRof9gUqeqdr2YzDpYZanprafWrtzSY+lZT/TH690xve0Pr4U63f/P7QpU/lJ7YXmN+u2I+sabuGp372mZuOeuMbDGOWQSZjmVO5M0DMPCTU3jIPCybUrJqV3PVw2I0ji6BK9tC1hIbh6aEZzXzz9gxPvzLc/teaEz/w+n3rB9YS2z/7UXL+kb9Z0z5sUxi6JoxeFMaDI7kwTjW1oW1R2Ol34U13hgNUaX0Yet23/r7foYc01Cctdz7wdMeOA2taRiercHdVGMWO0RY6uEZRROXX37XuwMNsf1DBryRrQ7Blz/kt7BndXZULqqTriNtu4qWUn6b7h6IrZlO2EsftZWQSsMWJMdTmJvFBKm8vz57KLvBcVMYCMjz4PnOjcSxrSTLbsAFpvuJYQHyN18WJRbJGDF7yRNyDxOxx0hnzytNPDz3ooGOa1+Z+SH/s5lL6rQrihmU7GysbcyTDDbbGUso9yigppmL0d5GGxcfkKbgor+oYKIgtz0tXimLd+lxVVWJx8/Zlx0ZwJBe6qSRJrC+Kp3GvPMMZf6NYN9wuaGnlVmctisnY5uIdDWfLs+dRd7DVsmq5wyGdd7/e5+ims/DK/JOZLysXVLJRxTfllp+P8RPnIu3CJbZCH0Ge9A7DHDcSY8xHkzMy5k0RKDMyqry+cySwWLjbN/WCk5jYli9f7pQSMR5Z3ESYzLGpVoTu7ZsvBa0zqiBDoWbNC0ab8SF8CA8VoV/yR1LBU/1fX3Ic4Hth/xQfixgZi8uCnbH2AUUy12STV4WmGSG81sgS8iCGUIpCwlBxEaengsfjKMqfyAoS+Kpu3JbtuKlmyDX8+8cXjdh93U8vqr3n1hHJcrtzxJlcHiY8G3bmAwTvcZQau3FhLAUc5Jzb7Z+rHsBdc/OQpPmo8Lt1YajASKvCiIPCg41pkO42ftw7hbknhD8Idbhs4syDDqBmC3wWxv3kP/+74xecNzg5iP0N6PAKJeW4Mf0+/NPN4dBfhlPF4Fao2NzLl3VO3PWD6sfUbUg6agbVtExJnrUhi8+TYEsLQwlfv2nT0ioTYD0pFaUePCg8MaPm8Q3J0EfC7veF6U1dcReHhg3rQmrvyRe8I8LalpqhG2qGtqeHf1Qir78XPyOTn+3lEDAP374ipJgJVxVyETzN30lEjgpD0DP6SlZm2ukpk7Gu6zgRtcqEcXc6baxJoyfY5M9biP+WImBfmwByC950W7uBVFtbU81Izs82/1r9ed6QkZpfedB6yrvA74c/Fnc4bASbzJ+20AunpIYcvWERslaeZaiX3il4SvO1V0jP/INBOGVDn0XDJn2U2zVf7wxRxdww1q54cCGjzevo7PGMSy7AbusoSEit9JSOECRCuPAf/SjdmQ9DQ6JWWZF0+pYIW9zILzrm6WbEOppJpHv9Fy3qLM5PEY4bOdglQZy7/OxCSiRDybILTdjuqRecJJzjj370o8MPP/zmm28+8MAD+SfhyIMPPijg5HbPmvwGeo3thvWqmHQMETOU6cYLxpO0f4NbPqNe0tf6V7Q3C3peipHKz92VGmpeM78YJBaILvIpzi+UE3ST2fzl5s47p8jbLnQbqsuR9Nbf3FGrie8nz53ed8rjB8wK112z6P7nll75wJSO+xaFSSCLWQ6CAWXWNaaq4hKkGEKM26qwy2HxO8P3ZoWbloeJUJGtbevDcGDPQbNDQ8uTNbu9/p21O0wR1bEt3RrEaz0RxgkM4rjdTXRLHw3nrQ6jcjG4gRWWuw17HCWkvgm7ZshBux++45//PnfPtcmwgakhjNoHzKqPPtfdueQQACd6Akw5GtjFwLQo/lJnhouPCdcMTGyaY2bb4apw2tV1x6+rGUHSjOpoXdnS1JICxE7yNH3Y/AG77rrPsILw311Juv9DGJy1M6LN4sJvD0cx5NU0+wFzBZX1EJcPkGFDuL1yRxyR5ZoOFbK8q1Hd9+MVbEGgwhnxws2CoZU9IAcDiYJEjETzNA0c0ZvpACgMJPA1j4HZo9Ve8PU2yAnOcrXNzwjkppEFBSZOTJUWdFr4qeGECQ54FyAGw94qFEuLM6yynpJpJkE+e3bqkuxvPtm3KLr6+ef32JPoEehEpKjinaQUJxZLEqDiKuXnXOHa4xHUZpm4g//IyHGEiynC4NSt4lXy7/bVrx6JjI3lFo+xCiUW/1Sy8hHZgLmKUyWYjPIslhtz8JSKuRM5EG/aDUelam0Qqy2NgS4r8c+2e+oFJ5199tlMbNdcc8306dOPOeaYGAL72WeffZ7Pd3vBu8FGJ6PKSiiOG1OVr2Sq16wfJ73gvbO1KgAYkPUZSNqSbG1ZB31ilDaKJVNhHDlmPSKKxYGeKa4pfSU/iA3TDflBYEhZcnZTH3WjcrDHqhqSz+6zxrYf8q55T7U9PPmOTd/9xsSwwLRMH+Hokr+Hg446Z2aJfJRtTcCk1FWJ4aGZc1KWUnjJ7Hpc7YrJ0+cPWb5jWJ6k5oS8ST1fCcDraHxYKWYQocMtpjGsPnSPdYe9ZUrdgLpQGwYevN+r3/3Ekovu/uPTey5NRgqn3VI7lH0wdSjKUcYQO0xpJmxcJ1mtes3XXZScEq47KVzZGgZAY+JtcjA/NVzmlIi7hxwiBkJDmPCxjr/86IlXrggcamoGhKZXhgdG1617NBlIHlQmJdr5r+iMhAyAgPPa6pdOADY6rDw7/JqZ8sbwyofC3l0KrOzR7gtW1BtvTHcMCScRCb/ztVbdSXNXUJFR5CNZV88UJOn+ijNgh7EEjdF55xPwZI8SL4JeM8l/quBaPQFHf6kcsr2cBWmyr6QpXCUgE4qFYqlVASRHo0YYA0wOlbNTWCQCUR68L5LFTvdXAm3vtbZEvi11AAfs5YUqIL9SGlFoifVAVYw/EBXmn3de6VWHhTEzUwQKiq6mAgUl+qryJIXmK9eIRTLMSFdSpAGyboJlXm1qMI+YhXIrlHSiULSVOVmzFUkRmqNKGihnI1kFIvQBmOAeA9t9izE6NnXIiM2X+VK0JzyMmrZpuQOAhbfY6rRkUfuVP1/dsHF504CRx79t7PTdentLt3oNembYS/GiAHz4wx8WAnvMmDHR6Obxk046KZ6P2zOr7fkbTXX+i2qoIYPJWO+n7YYDOpTRbauQGYcYgH5MQNQPjA6RjJyzzkpPnSRmSGjmG5LGYg6uEnjGzCVB/kgrqIzcuMX0iUyCZplhH93/ug1nL73od9OSxzjrPFx31HG/eus+r0/N6IVEhAJxUSwX/pZ+zwdAI9tX1vz8++H2G1KxU0ZgSs/CdWE455qak/6ezGDvO7jmodd/9PBd9mxg/EszHD1q3Jmv+6f9Htv1hrV/ntN834opG2vGTRs0aPiI1PuH3gJDsIX5RsxAPjGsEhpF/GQkKtLB4T74YW0aEKgm5wM2fFxYsGv7E7esO/C59gHDhw5+bNhuU8Ozx4RbxoRVjuxg1Lu3Y5/lHcNW9TxLLsuThCDOWU7JV2t9sAzF911/+eRRpqUKbJpDw/JTwq8OCzf+LJz+l3BYB5DWg2edz2kUExu5bjAQWkijDJhywQUy40t+w3N5dZee+9rdP7gHU7IPWuBlRP6JTy3odk+cl/1e1QVuUNjAjpRDgHs1gaPyRzXQwMZk8em9iKAfnuAcw/eO2kmsAaMPVvjiF1N0VSVGoUyiRhIFoBxh8mmnpdiI8xZLOpAHmmAOc7P7irM0oD/WNDWxwtEdurtXGF2uOPc9bkpBEfe4iILDReQGVyTgQ0F0fliBLQJ8mxMMcimNQH/9urUoli5b9dEuH2MA59VBTbAdQ4wuCajfMDPfcBkrP21aGj2cTtcsBMAZAEDSVqxhbOnCeU2/+9Cf6/9w05CO5UnNyJtuPmTev5306pNGx3dwa3GjT/n0gpM+97nPXX/99dOwJ4+Ew8779rK45IfI0GYMGVXI8DLQzaHGSj9tNxwwj2TSaKs0ykKZSp+Ucvi8cxt8BZIMG56/tO5xEWksmU8zIJUvTorrYEqKLgLFP1W4o13jdhr22i8dsemD+w1N1vOS3qt25NR9x9QyexWT1GZ30qkiUZA5RfW2cNiCO3ba4e4lsyfdMVlLPFuqAewzRw694YCBj69tH8XiN2zghsa9X1k/uEsCCPk/YcLo1za+ZtbGvVfWPPHckOa2eqo106K5m8cG6EBJYOIm5GAL7FK7/AqSaHzMY2CnWGtoCSCzmG9LOlo2pA26YtkeDWHd4jB5cPoeD1gaRjU78BeoKuNZT1XM05b0pZzw1hMt9BwUHtCgXsh78XvAFL7hV4RT9wr37x/ueHMYdVOYyd86p20qwWrtuuCCVI8iWpU8fXhb02SwA5biYsn+6FF6LoU7nWXJ5Ne/TpWUjqjLoJIu0jT+ws6ajRi0ZL6Vb86aFXyYzHhA/9u/dZ6wVvmR4l/BAr7VXofMMQ5csJ/AB6mbmgvV402xTNVn+r0cEe2wTrEaqSC9EWUVYC3E/KffvYBUg0LF0o5YzAjy5L5eIO54d0EJbNxxQqi6OwoK7OzHbIwZtKoacwNTdIrikJtxAGCm8QDGGXKwi5TWUQWr9MIyqvuu3w1aNVEHnNRkdjQcNikpGmcUxyfpyCNT/u+/f6qGzBvknWVICUIBoxZs6qySst26pMn/86a/Hnnvf4nuxE9xcvJU8pcHbnys5ZWz39EwJid9t2551eXWC06aN29eR4XhWV0Z20Equye4RFhmx/FhqJkjqI6z2Wc7aGN/E6qAB31gkknfJGIyEm3SgeEmXDOLD6WIWG0EVZyC+5BjSOduonQzSLljJg4MTFA5quR5aUzz7bS0LE8E1gNh5rfD+4V1bktqa9uSy5997vPDv7zHsEfKTep1gweMrV0+tpaA2hQOelUYNji1W3upiCa6NRtmDjpo4LiRk8eGibum4iGu47WXjgGj2E3w0zRNtUPNw1gQhU2sY3MY9ETYef9wm8k/nsLBKMcT66kwJafO6UzcHIY7d6t8s3r8QttB9cKpBUXHWx70lBD6lMxWusrkY7X48JiwfGkYe384cHJYmLNvtrWGtpzrejd8yYrRKbakRU9H2jJfiSiKFmKSo5IRogi6n89/vq9Dpbss8A5UgmaiEwnpCKPbtAsBaMXmka7RC/J0AZr4sF45K8EpH9EcU3223IcNgQwn5T+ox+l1ZGvKxWdOaXzCjMr8fs/S0/lxK8SuciQTJkgjTnBwSiOdGAcY4YYV7OC44QMuuAM8qdiPf5yaBSlOSpZYrqAK9yOkgFRi6fCQIR1JA7FO6VQ72gLM/eEPqQJVJemW/C2YKzAnuyO3kjWMxeX/JB9eIlhBb82+xjEAxzK/ERdefRvx6LTkbzSWI09lNS+XZrPvP/RAx573/koQtSVhohVdUxgizusrnr3sv75w8qcvLKX/3uyS+vJgeWZ05fLCHqPWVYsX+D9kzShroyk0TYhw/TOXMSdXGEwvcI37i+8jB8yP5fZj9zGnzuROLotk0qEEsm6Dmc4/P93oxLM1m+aqz9zER61Czr397amxI6r0q3+82pRW2ZnLTJlnVoTGy8LrHwozTGEAAR/qh8OuX1z3/9pPOTVdQOQvM13/67+mjScHGCPJbdeOb3Cc23vek/LCwXXOZOEBRHWwZo02moVN6JG8X8wlgJG/UQoy9LDUYGkeWdLWXhJOnZeugZ9ygN2ksFDF7giz56QxCDZzwcuyprMQSWOdbfO2LiPbwALraaMFYM3qGSsjoNQOYRFrBrjG2/2ZMIlOa0DuzJaYoOCvnAlIYpvciqRdcCFNjxGiCCXaKUJ8bjZhP0ypCGXRaFrasZvwUAYONo/0CJ0HUJLfdiKWAoY7VDQgVp+zWsEE5UiPe3Go9DjK2FysFThTUIT66CbmxQrKJJzUcLM356fICuX6II8bctSWdDZ45auP5sBJ7F+4BF1J4KMX/FQNyackKZGy0NvA/Q84ppAD+wBlXkpW4MaY817+5V/S3jHINRNE8y7SJxkGKiDbWBOZZwoq17EhsUQJMoptzL9jxvAgOBi1SnTYcH/2uLfTiwb+Rj5k+TzPF+9+/arp4UkefkCSoumthX8fG5b9/jvPPs81yS+ua0LKv5d3vXr1aqEm6cXz7nVecl0qvrkd3yE7+D/69NP2ygGGgK1F0AJPi3wyH5kBwQNC0cS9GUQyZfuhAAwrP9oOW4SYoqwRFWf5S5DnC7A+lKJaRBYUxpWd3KhIK8Pke8L+DFgWuhKyJEwMix1B//igBXve8+POqV1VsrWqDdA8QdRV5egiTMkqTZNkSiYE1J7Zw8FXJB6lxPvel8rG/Am+Z2XgKzYCEiVPe1EzN+z6sfCfbwuXvircbWK9LhxzdTh+jVgBW0C6CT9YqWxOJEcVGon8tu4nwP7xYNt7Xrv41nkT7e8bH5ZNDc8MCi1Ovjsu/PmAcD+Vm6NRBDkqp1IyJEgsaoOesK9Hja3KoARakM0gLNQDUTRSYVJN5Xnnb0Z+qb4BbnOaRymBkN40SZbxUitdnBrGvVelf+5512LVSTIwBEQNR3qWEzTITR1VbCHKf5RmSPNVT7KoM8Z5CNVfn0jQQ/5X18yswDEySP2K9Hv25rpp2JYkupaSP6kwwzG3RXUAmLxtVg1eNYkNAEsgrNDRXMcsqLwoNkVmINKz6qAC4FqGqkuWXnzTI3KL8ChmqCAWN28hoOb989WqQNOsVirAzeKct8WdY14/ZOOFQweHTeu7cqc74/Aycc9qNcFdz23N/73gpMGDB3/5y18e0W9e2po878/rxcgBMxHkYZdQNnVuXi1Nx6ZCVpuCuZtQd9MaMZtq+5q/GS2SIlRS4BlTOfltxke2ghMGnMHFfDKf9oHkBbJwoRKdqTeE1JWtOJD1zrnq+iomUrv1X8tjT6ZSRWycAoIFrFU13jzNuxhFLkf5kyUmGTjsaB5swuRWvhksMqyZXJfyqGZemP6V8B8CWnJO6vWY27wHK11ir4FRriLPPVezxz71983f1JEko0KqGOFIfnj429HhL7bt7xUeHRNWO7nFRM+dI8WTPQkPfABccrEcKR16pE2hbyhFEHdhtlky8s+YIbZ1L3PeloAk1QANjzgi1aLpn5JEUXjqqam5qnoCevpqR2ZAtDudkky7IMhyEBM6NJatfHCYaS+CDIgke/uyi1hbX1m4UByYLrgx8U+H/CIkkgATfCTwt9yQ8KAqYUWxbzv7g0nAfjHrJfokg139QSWAG0vhSzm7VklgxZCI7yLZ607UAPkb81eEntUoNUFZnbML9+WpzlJGDaiiLUbMFV4sVhHtUoRBpXV6lke29Qut2wtOX/7m4E9/+6Q3J79yZrMDLU0yItzOCUf+4obyLvrbvtK946Rjjz220RzXT/0c2K45YGZh8jDRM7Bm001fW2z1b/sSdw3KlAJi9bA7Os50BT/1+lXd8quUTfF5CpU0AdjEHGweZBjqA9G/g1fUSPllVHx+WFg5IzyVUynVU6WYzlaG0TuH+VMHdocM6JEBsfCtbxVsEuuRIPuCQbxFgD5nsZq5y8hkC31uNyUp5wxU8pc+3yQLOY9bi3OgItLwidBSI3KLVINC3nt23eLF4zeljKt5JOw6Iaw4KNx7aLhrXSrEm3cJ/zg7/Oz/wmmPhZ16dmB3TeRmclVEBWJmJe0ETmQlKSBSkmYkjVfVRekpGaGmI0kfibCAOuHGG1NFRdXd25VX139KKUYirkK4wcO3HJHHlHxx11i5NPn3gQkmPK57fSX1iQHHyz0IQwjzQ/dPfZu9LJqP2xWYUPATSKHHeYzpa6MSM93xJnq5vOMS81frue2xszqf+lRqNeOinh+XSy9HEMnxSF/Q5aiMDoV1ZKvCQIxh4EIRfnWhRKQIUMZQdEdbzDDuqI9uxQcwK1bbUzKUwNfYTLnFZYhBazgaD5CZpwzmSBEhGdhc6U19WirNi4JOf+ucX6zcs+Pvg8O65jD43prDl5/8rmRgV/yMF6KKFV/QF6JC/WX2c+CF4gBnCICBRprPjIUd3UefYI2p6owz0hi75jLXBcRSYHWbzdoFv1b+WjCDl0ssmc1BYpz0ASeZpC2cuchWWUau7Maw6KTwp8VhopNJLPhs4mkMa08Pvxw9rsxE+9Ofds765aqefx+PbD3iSMJfo5RYJl3Ud/M4mV9O5Ws9aJtYxLvUHlwSITOSBhFU6hCVedQ5jrNysz0MWhQm3B/2FxPnybDbMeGP68JoUcv3DA8vD8NWhxEbw7BiZymt4ApDBFYWUYIeqUMMQZRfbWozBpk85VkyfJhTt2pGjU5rThBS4flQG2wJuyge+IQ5ZIPioTKRteyrGGUcGvAlxxTGQkj2k9MMwVXFb0rlIqr5FaPEFACS8kll+soEKMQMgJNcniOMoCSGM9inQBBA2XRRQLygIHy/2gTAiRBc078sZeCI+GLUYPKMuihoJjoMySEyKlbPX5MPQBMnH1n5VVf6GCQAol8pCNUKTuJyLoELYIjuE6TzNvpKgasUeEsR7vvrJ8BLoUYvZZJSrLJkJY2/YkrZdMlwT9X0wpLx8JnvTP7lbv+vY9ODw8KS1jCqZsA+Hzpz8vCR3YuB57+GveCkr3/960Ng0X7q58DLgAMmEfFszj8/hRowjfnFhEt1YeeR2adXsk/EvkiuNSXJ+9/XabpkPpVvEoo8Q9U2zsWVE6e/mkF5q8ala++pO1MMDu2vDn8aGjbdGWYuDY3jwsqDw52H19wY9j+vRB6arYiSMrNE6q5bWsJW9N3vdn3v/m9+r9Y82P1Q3664IsG7tB2R4AMqBEiFRpB1hocv50zDo6tNnaCYEkesBI98OUy+Jbx6U6hvDfXQJBTUlqp8irBzTkB2ZVKphgYPuVuMkzyThIb8JwcNrn3Tm8Oxx6bWFmMY1MtXOuanrP6aWgVQ6BUkyRCMYLymeVIunFRMGsJ1D5Jgm3aeQbWjtDijinf4k+mjYqqG1flPSS92A8zBBYrjnOUHJU2ss5aCRMaAEZplK+y4WJcIyqGB89day8sIXvMEimZQjwNAwAryYPZs/Jrezd3PfyPBaB+zE8CqxEiy9WJFRREVMn2VsmiqPAjJqbOc2f6UawAAWBaBkBagZn5gGFcH16oBwMFY8qdH18W8oxT0wtKw4bVnf9rJH0fFanT+e0Hr1AtOOn27P+DuBeV+f+EvQg6YcRh8Ills8YOxBDTt8v7JP2ykuOZwkqm/HJnRnocJyORIhJuXy8G1wuqZa/On5MKfy34fEZpeHf68X7iNmmRIWD8yrK2dtnN429tKPOAIFFN4nyhKj3y7Rd7jZnw4xvp4GxFpxDvYx0iIRLqzpFjKAx8EJz8zciVfwhXUZEMY9edweE1oY27jIuLXXLSCEjiJ1KS/JMAqk2SGFoVEb1QDG6m2oajCdksZsVuFIjioMiui12gvyR/QwVqCH0zmFF8hT2OTUGeoAoult3qBAKp5ifSOZ7cKefej3U2LCrqJVoyaDS7MFlFib2ZeiRZX9En4rzKmEYjEnUg0QNVXL2OjR/SCoQgDYYJXCh6l5HPIkAytd5Cud+1X77QSPUK3DSS5T4fkPpxnAQIP+UkCyE9ubvrVg+yzzLva1U8FHOgFJxWk7v/az4GXFQfMzkSOqYoPpg291AlWXfT59N7FZFmWzZLFv5pkJXgeyEqx+lk4nTLp0EzA2WRfdRVFjxwd1vqkT1A42Jw9bVrh06QET41ssi/8ueJ3qKQUkVi2xFm796GZXfkQLQQbXyn1KknKhJDYTzOQpO7W5fbncZpiQCGzLcR7xZZsYXnmsJJFpTdhLzipVxSipcw31ZDe55FP/8HphEF18xhfUJBerWwWLEhf4avzSVgwK7wIWM1yRxdlLxudk30V0DIrp9cHTBRKqmSwpQolbvlPeCgWpWpYMhWQ1UjxTW8SMO3cdP7aBgwUQgnkpnxid5TsFACo5H0lxuHh14h+rBOopmAvfQ10imTLTE1jqzKAGl6Jj2WCksz2OlMWfRLQHIm1Dh4Sk1MXMIZbEqheJBwGtvq6ma7z4e39XxeTtvd29revnwObzQFTmGWZT9wVwoQvAkqBoLVQrmyYIHfJYDI+rvw2uzKVH1RVsMcasVoyB1uT2l9Gum42CacjOh6Nh+ILyDFmPFQ3j+RZhuAkuMH+8D5BJfukPELGWEbbV0f1Qvb4kBbcZfQgn2KuJ9bT8G6mt9BfUeZhFaFCXcf0Vk6klalv6dsW8RpS2SU5Pqm46lVydDCMxXq12HumdD0q3tWltCaEbp8IM0uS8a8hRHXxioLeCELCXj2rW3Gbn01mNNQLPMRZQmloIm4omX+8KXNSv1csWyGHgp+wNNMGFfyUfWWdFEXChn8EpnD6iZU3zDQHGw0eaKnciK0wouISRg4wENwDqcsE9hIdHltATx2EJ0ashvvJ20wNhtU4aVOECugOXyXwK86Yx3yl9gO2IindqObNDUhVT5f9uv0rX1h30ilDPvflMv1dfV4v8pScELcn2rhx48yZM+fOnbs9Naq/LS8UBxYvTq67Lnn88R7lf//7yejRXHd7fF7xiuSWW3okK/iyenXy0Y8mEycmdRQNPZ/dil8POSSZM6eg5Cq+LliQfPzjSWNjUl+fTJqUnHtuYt9UldWS8qGHypZx770lmFVNzth00UVls02S9vbk5puTyZOrrebXvpZ02L6fo7a25NZbkw9+MHnzm5MLL0w2bkx/yn4tKFTjXvWqZPbs5OSTk5NOSmbM6ANvKjS0tjY577xk/fqC0kp/VcO3vrXaluoQFW5qqjZ9hUr6SVYF4790FXvebWlJDj+8dAV07AknJE891eOByy9Ppkwpnb6ger/5TY8HS35ZtSr5xCeqyq0g83JfddYPflCyqM6bj/5/9u4EzpKquh/4fd3T07PvAzPDAA3DMrIJiIAgOICCoEZRIaIoajSgcQkqLkmMaDQxwRgMCW74F1wQBXEXFDGoIAKibLKvwz7Mvm/d7/+tvj3V1VX1Xr/u2Wfe+bzpqVfv1q17z711zu+ec+6pe6snnFAdNqza1iY/fTJDxowRUJ88T7XqHMR51apQ/e4yYkR1332rRx5ZPeaY6k9/mm+byXzPPcn0nj49adXo0dV99qkef3x15szqjBlVwuqAA5KZ3NFRPeigpJL99ktO/uY3+Xpqfb/oS6snT5KlgHcz+VQqq856x8JahbfM84sXL25vb1+yZEkjzdvcIVtbOIpsNm975YBFm7W+hZf0Nlb8ojLlzLWLR6Y7OR65ISzRUusJwwBvDt9cHWK0sBoW+WSVnF5Yp/zgfhLqG7OkDOxyS8vzzktWlKxkei5+pK5JKpX9yV2sT+ss8G0TElQyCLL6liGqNlkc2wx37rn979DROgPEmJSy3RkDyk94+eVJxLFVuJ/SX3P3xFKRHNFYqExcl+fKDPSrxtvtL2GmyVCf+Fb4m5hzpFZokNgGOIl4OzcIcUHyDA6UPCCi8Jlgi8Q6ItrPs8CqFImFSQrS0rjv4uWNWCexS5D1BtxMJ4S/tC+xeXp08cWJuctE4qmPtjT2Hh/DtwHJyEZzlDuawzG0iNFI6CRrVjQ7xduxM7GYcsbxh7N/m29MYkxxLFJIOxlNOeMMk8eXaJo1K5x/fuK2a4Tuu696+ZWtz83tBQ/Vats3vzXyC59fN6KN1LJVlWn63baq4Wo2dpNwgICjRL2yAGygGskmoR4CIxykwoic4uMnZTjUSBlqz3F98loNkTGuEi9pHwoZqrY69vb6tZX+ytQPz3mZ5SB0W2+FXl5gJ7r+FwhCylLy9cQTK3XiTbBPPhmxXcT5QKm+IzMknLS3XJit11mIGcqRO1MPgAgmGyOqbnDkLsbaYEX9p0JQaaBR5LExBkVjaHG5QKHwRvZgAxBegJNCimIX1FycQsrj+vqTTXbyREOZgyCPA2XM6VPEChpsyQFYxORJfEml29NKb4p1VL4nNCIGQ8yLCp04yBL/kVg0M2igI5WtJB4bfW9lgbNLSV+0X8i8bsL2WuVM/OTKa2Hp+Vyx+l9jrw26QbGisWPDWNtYoGaw24pOa9Hddyef6EHGBIiKHxCXYoaCOKAGCE6SstXLuYmmeGH9u8dfv//9rl/80mEfJ/uylS1Pz+k7Bo3UtZWUaeKkrWSgms3chBwQVCOFUoxAIoCiKsoFJBFY1mEyjtAlUo+wyDRCVqUyuwjwpCdkCGLBoUiIcndZH9LCSOqhWcVSrBdOIjJtg5buOoZ4rGtcsY3OPPai06eM2XF4ndbLr2d5a8Mxud44gSciVPsjCsAmc4Ffci0JDcFJ8b9QER2sE1EzwTf0nMEaBGGACg2xuBnr78hnB/RNUf3Xqd+FlJaE55okLJoKT4eseBUsRflptsIGYfFibF43wIXSdHNuZrrK5KwVnWNfQsyoFEcVl1gXmBmiAs5Wj7eC5cWDD5RU5RZCwTBNX0pJm71tDU7STpYkD0KDJBeUvfeSurtQFyh7Jl6o3jOY0/Ti5UAoIGZwZIAii2AI1kfguEgKwB8e57vuStqjU9pQC9TG2oqVNH4mzhn1+AgtIjogHhPAiHu5HjzkqY2bHyI8xRykSZ6IOKkMTYp6XaVCxjwzLce6/ppUiodqTtH+atsKfm/ipK1gkJpN3JQcIIPY9gm+VK6lB9lmOGkxx1BCnQyIxBGzEPDTyRFH6HuPAZcKC/n6U2wneV1LOQ3gFnAWPYMLhGhtUOCVU9+4ZPjpr6nM2LNu3aKjRZYypmVTABHSErkwrBXJ3WUkmTWr+EvPGV3VSZLeuye7fVjco94GT3mwf1AV9vLYo2SAHFPGjDepJnApqxvMxptGf1ClVtVFYrEAZKPTTTMhPZVTgYabSrbDyHp9QHx21XHHJTawOoTTFJ4pAfAxXDE4PfjH+dUqiJfTTL06qQiSInLC3SL0cdI7Ae0pM7AscLpjkxSIz7XHYpolJdnquIlTvmV/rXOMb0AqW6Q6TcU65HUiACiIZk3iqgYJ5M2SW4j+ZlCxM06DswQRnnhigtUGhGjTGrAxzjKTkf+9lDTerBOBbi4hDHev+BgWy9c6XyxZ60xag9Ex93w1+lC7OWyY5LQE2gyoX2FxCwNcjeBeYSXt8OB609QIlbRWDcRRipxq3bdwPq6YeidhocC2dqKJk7a1Ed1W++ORJtmpLstfy7vss002saAAN2QB90rc5z5oPliK2dZLL/ZLylgTyzgyUCLIiDOWfJ2is8UW1H6tRM6IUy6b1JMSpV5fGacl6x2Qr5CKfdhUNyTSl9zNDh6fx8PuN90z7sC7WzrWvY2N5iCL4T/DJDCJpk8UrX/0my/MaFwsfHCyDbIzKQFpKp2SptvmBiHxHWpDKd1yi4jTrmUrWidPTDCLqoYMIe5t84mEG+eck9gYrJUhIXgvrYnCAMyocI4STYWiaFMNgQazZGQlgKB9tVTvTTZGBeNl1HxYmDRcPAdbRQ6mZCtJjw23NvJu1I9G0jbQwVtmqfY4oDM6uvacf18lHNKdfimtr96BC2OTBKZEk0O2tJ7a02RoACBKVEnaFHP0zltyU9OLBtvuDlw2aCVNb0H1SlOO+djbL1HqeOJeZgdrjSHIWmKcB0kh3ezcrlUnZCkvqN5lpyprCnOvseNDN9YDInd3XyOiVXYOGvFSgjiZbxWT6RH+WH/DcOldiidxzE0R0aeDBtSMZcNj2TKsrJVwkvnvtdlYqi8KG0oPtPQW4JR5iFFa6ycQsP60LN59zxnCwyvZp1YZNxnSok3dJqziNVv5mSZO2soHcPtoPoVHt8nbxu5C/NnFLbw0Rh1aUFoK+yniJI4tgUSUck7zNc4n0Q/W9A2SHeNSmPQXSFOzMnLKteIGHJTpgwz86anDmV6olL3KMYogEtjAEx0hQ9lXSPlYkk2HPBUfo1g/5Eodo0g5fiAL3Bw2bNnsucPXJukdWXJobovVn4Q33l7dE7tYSkBV3hBWHGNELdG+7ustp5K4EOtJP9l8pMRRrZ/hF3jWWNKQElFakus//OsC42cUsxov09b5X77sq98YMefmqTa8jRhdfdkRVx199rOV4/rYPXRW9QIvigSVCsNnDYreCm1mWcEuTcsSNc/IxxnkJ7XBirAL5GYPNuMTrWMGAniYHHPVxKV5tgbHcUT0XUe9yLbW+4nFXMd7YYDUXFnn5OxHV48N7VBqDedV7obJ1ziLzCh3pBo5eSlyJzUDjmRak9JCr52BWmVujFkJ6FRWE4sQPzFOuJCihe0GSmAl3dwISEpr1hLKns6OL+LAc1OURud0NUtxXvtZB3NaOb08PTBJDauOZEkX4CegEFeNl5qRcYlcypY0HyLyALjNemjDmGqAVEPifvxaSpoHySFdyJaJQ1+8S2klgzipZs+HFrqRhxpM1BJALWb54irVWU+SZYApas4DzZ5QT6JFFCwuhBGoiqKAMc8jCGI2jpaOfWnLcUev/MalbZkMYdVZR3W+6fQabBpED7ewS7bZjm1hfG42Z/AcILZoXzlCUllJEFsV0a1kPfkobYmlsKed1BDrYKMNq5JXNwyOSBwmpQYJJrBqr4OTKACN52IDPMgv+hU2yBI5K9sKiUzw6SnZh7IL62zh7uNeqJSV+EQnMEOvy6pC9xOOEKQPBRk5Q8Er7xaW6fQ9remOdIM7RseTA74k3INKTz658upX7VixHvfCDhxRy5Ahix9b854THzw1/L/dwoPPhmk/C6//XnjNkhETGMO8c5QDgjZiJEBuBOrQmoQ4lED3JOR+jnq+dJ/xB/rgfaHVkdYT7ThVgxbe+JdPnT/5mnunzA/jvHy3dXHXb3/x+MfG3H9ix0ON7KrTQSZJJi0qDYzTSGc02AtNYTNdTMlLi+Og4I/mGF/8NIh0CWBkskUfHG0KC8JYtBGjYDQnGIioxtwFXqGkGciYvUr1ENDGLgVfUrQpWZq3hdU6OCysWhDGtlX4ckClAchqIwh8wjreXCtOKLaHavziF5Pux3vpr4dIBBINaobAqExN0SlTewTSNpYfeBzY4QZBWgg8U+cgmmnDmhXzNwKRDFR42whZLOVwkqvMdoOrX2DBe96TTHj3QkaftdToWAiQJEbKM6iYR4a3zurCM3jwwck0BhxrkTFlX4QvzQfVerRdjuItal21/uf1wuzSBR93NKs9+zCQJZA5iQ+C4d75ziSlp74bbl3zk8fco2annhimhAAAQABJREFUIfmGpYCpgSY/L7oomQBkQvYRqNPIiRMrn/zXoe/7YOW0k1feP7tt8ti1V13bPn5c2+4z8obnOpVsXT8N4NnbujrWbO02wwHaVwBsBEnkAiKGQAGRFjSQ5ZFtscQcJUsQEFWgEuMTARELD5QPsJeFF2NMI0SyEEAp0ZTEljOkGFlGBAubiKs9DSOYSC6Aj1RKiar2ASG0lmh23DjlCkfpLDokCutYT05ku5G2CXmmSslWCQlBGZpAl0WvOw83uIS2O+GEygUXjNlh796Amh33CpPfv88bv3DM8LBGZNLSMKJtxLAJEypELfSQu6nUU+q/844KEFBrr1DSQt12+zq6KMOOi/9nwU/v63gyTLH5zOmWsPaW6pj//v6qqcN+cuAzv0ymCP1A6tdwk+gX9EORGKA4N3CDCtF+UDAFu0waWMH3SmsatQjeFGPsoYEUNrIpMcDw64E7MbUgoBntmswzjBnMZ8CKGvC8SCY2Rwl7SYaq3nZimb468V9UBZC0hbU7Vx9/tLJbe1jptqurQ1sl0WmrrFrTmhvZTCXJCKpWAnHjgitsSNovCYLXnaZzRi8YAlm5ODmjgqQ1G0FILoTFY5i2Jw70p6FNcszU9wiUs41p8NgQaJsnCOtSUzHA6umu09Ns5XW2NOqXoECzjIENrFeS9ABwjYv982YNOKX9GgBAwMFiwyEko1+fgCT4EldNqrjYUL7B1tavuf6vZi/0444mrefOHXVNpzxxCCL0/LKpg3FZUeMnF+qjWaq/8SkwMXTZxBYx2SBOUs+uu7Ya9N/d3L62s9La2rrjjuRyt2hO7r8NUtnjuw12s9mlrZgDBKhlJYq6LR4QDcwAPpFgI4skpgsCkWggrImDxh/7nlq6/4OTiGnvPM+qw2yB7DETEUFz3XXh3HMTkOESN+X04RaUTok5JkUPMBBh+qUvJYYPboWUiFqqlEpm6SkQrRmlbkMLNTX0q6WwRUvoTmvrlLBXoFWWmE9iwp4vf7kXCOrav31uyEOzx1NdxmKnCYlcBrOspzO6odoq71zo9ObXubOXjQpPPvzAXssPXDlieFci1NMhzN6sseM11//h1jvGP16dtiYBSTiDEhPQdZ0HP/KNbx8QftnDI/BEImopwgvk5oxZeoEDUaMoYnbBrwYxEhjENiYMORpdqBwfXFWeHo06KVuxqvheqXZbjWAmVkxKV0nUb1+ZMSKT0wrxrdprN6osC2NXhAVfC2d9NHz2vpaZoatlXMuy1//VikOPHXP+l4dje30yKB4KgIZSFGwE+hv9LClg0gKFsE6DZLjNYTvO0qcDGoukv5lp0GB9vcVci8+eBWaeuPbg68y+Zba3aI0jlpI6FE0mqcEpjk4EdlZZvgo2wgpDDNlL3eXWwqfqE2nDlYmHstmzh+FJkQPryZb6DTD9zGd4SBfSKa1VRI32mIf6AvGb9iyabLXOMD55uqHAyAFnXA7mqsqFA6IddmxILg2ozi2zcBMnbZnj0mxVLwc8z55kAjqlojDyE92vGNO9kgNKB5JWGw8IDhnquMP+5V96UU6ujK9a5V4iJwRkUEIM75HoV6YF0bhZSmUl4cXIlMVJEJJfLQ3LcFKSt3ujknWpN6mMCGvAmiVh2NrEjNG7LmSBoEgswRnnokXEX7vWLrggWOgDENHRlhmOBCR1r6hb1LIyjJgbJvzXex748tnVWVPvPv3l855/1mFDDthvMAB2xYo7f3DP7LsPgn7VbOWPLcIzmF5o5+vCwUeEHXYMcxJe4SNHrDfyFmxURhagYTnjCdURH11wklXSEHBLGU1uR36fItEitQj4YJyAriJ0gNP4vKy2qa76pKURjaXFMgEfPeceD7vcH/Y6v/rBJ1t3vb1z73lDd16+ZNZ3fzycUaRB0iqq0YfuLFL0NBXPO6NffvXBIm5K+tgZLm+uuhQkZS/MTIPs6T7H+IyKJY1FfFUZ2GHlEEmx0oei5+e+/8XR7Huu95t6GFGMLwDkFgAQz5rG6JG/sLKZkPV+Ku81f4xbZXi7t1pHYLEgbpYnQ6kNuQFVoNjZPtcP9otmA0buS6T4a0R8InvBIClYzViLN7auOHU5ND/60WQFCDNZFRjWaADTPMd4nsKswbZoW76uiZO25dHdNvrGPCPM0CrWI00Q1JE7cdc5XSgsY32IBBEwQ3rCBARoSuQR5UsaWnjZJ2JVzfzAqZGCpLRk8SC2nCyzbCWd6R7HEBWXIt9crU5JcNgVvPugNyYpi2OKdxnQmaFh2Unht68MP9gj3Dc/TLouvPTK8KongkxQPctErYKHZHHEUgG/Wq6nGi/0AXP8hD8UcJaonu7NWf5SiRXhNYvD6JY1Xc8+MeHJ79714UW/OORjlfD8A7KXNHT8zDOPzQ6Lu4Ya/1g+qb3bqsTDuSwM73ZUravJjjtRPzKMF4iSAIKRIiwHDC20pv3kTEF0pxCrwRGeUFeRGOcE+vBnQWs1HIA9JalY+qk/E2DLF8PbAOkdVj83J0x5sHPPtb9pKyrj+s0GZ01p0K1I9kZphgfHbATstMfMRKa9k9wxeMKhw/roJxoXBGwcuxRvZ2lhMSOMKbWzKsPOASRxdeW237sjj22/ZjNPFgynEjXXIv5NSebjU+bufLOsgHqqO9HMBggWuSrjADNhfSsLvmGLmiPs0BLH5sNGpdgR7Xeg5dpgArMd8vAi4yjtLUTI68oyhy1R1HgmDLdpYI8nk7CmqkGv/RXRH9u/UZu99VbexElb79htLy2nzKztSDfbNIqyLMcFtnrioP4rRHKXlH4FhtyUBOQc4dQjixG4QHAzGEhYTRjRiMRoIyApvQWRZJuxvyQXUw3DTAyzTQtkD9rbW0a2tixb0bm66p1w3S3o+ZstNfjjY8ONZ4d/HxaWrQrtU8JTbw4XjQ2LzgvvXRl6FI5e48Mf/5hwAA7gjSJ5qUkROew1MKIosb5UtRXOmW67iAZ36ajv3EmLu0bcsXzG7/740P5/vK195t79h370rZcUH9YeRleWjKguXxxoA5akBDuqeXKYe2C4Y0zIZCrEXOFXZUQ3aLwQMdOJtQAC4AbVRw7cdSCpB4d1Xx15XlZRf+dEH9tz9M1vJvq7Fh17bKKfeDaj2qtVbF6Y9EzYpefXtT1TsVbh0vO6yZs8a1aSk8GsS0nbmBY4mExy4yug2/YuATfUrYH+8IcTLiETQBpPyxUaN2vWTetp8MBowUOeIH5qIAyY4AuDQhg57G6j73MEwGmz5tVBZsZOFww4j2ctVrOvpCAp3gLDYf34UDvjqzJF8miz/kLSLEa1yOJBfA9Ih4E+yYzsDl9zUH9Ya1XYyHmVkyGWbdGPDGo7Y+lidHBDZJI226Zntkfs6EBJT7EHFkiSzgz/GdgQpong5o5Upkm1ONDESbU40zy/pXCACGAkZ3Qh5UVtc7fXIUJKeKZLBkHknVADEpMMErMCbLEY2V4OD1l9EkMCPKkZEhlcE5Zx8cWNisIoMWNHhC4hSpoSFf9bR5jqiBcnUSqrlpT0hlyjaepcXnJN31MnhytGhiVPh6kRhI0Li44NV18ZTv5L2DsW1GBqzNJf3IaYX8xBTgKs5KwFaOHuydtzuykZANUy87D3DA1rGJkWrR3x1JJxS598tp2xgowfEE2aNPOAYc/76dNPLZqg2uVhhArZrsaGxS8JNx0bfjUmZOx+MCxtX4O0n87I/g5TSbGTtDdPCRTLn2v4u2kjnBbarkWaAW6aSErWoWdDnw1XBZ7XuTT5CTPgJH20g8y8tXXcUJrh/I92P8FzvJCRPFzi+mU/En7O4hJBUs9vITFX+AyasN3WRoZelQOIEW/BRs7XIfBOYI1HoFavdcQUBQu8p69WVaxipZc7aQ67CltKLUBmvuHjJccTeLqUNABWAyJBJUYdUIkIUnPxjm5UPFlaZ52TKnHHWD/4aCHnjh4mkQYcZzxoGKsZeqTxWejjKg1TQHmWYMfx+WVwIuhiQFid+27nPzVx0nY+AbaO7nvgvVCV0525qD5OGnR/bBOjG2yUs92JTOECkICESYDLjyYDoaKMI1+EPENshNRApR4ZR+7rhQtFw1iy11qdK0mdCHuy/tMwoKpIqrJnnxVEqMpAW9JdW3WvcC9L0jpLFaAxfGKYOzU8leIk1UKHXI00K6hEsFIJiBS2Ni1b5VN6SRD3OntSYlgSNbEqDEvefFUNI1uXt48f2Y8no9hVZ4YN2+XVL3zDgzetvfTOW5btMTdMhJN2D7OPC9e/KvxgRrijj7YVzz8QzyvDWG0EMHioxBoBWNcnCp4rBE4y4qh0HItBS/XrTH9VJ93p2YEzeK8YRUxmUXeO41SUO8fOtSxpAHelkubnBiGWVzPZU2PaQzx22keKHqJ+b8FxyTpC8VufZFcFnlA/wVsgoA9Nb8xrkbxKdShbbd9i9gp02dl5+aVD9923lUCIY9S3TIK5QSiJITwOnlMPCNQSvYpxNF2VDmv2OFdPg19jDbFCdxFLh7dAEsSJJ4xJ/pJXFntYzaUISJkD+ugM+xxkbMR5G9UD2DnPJ2szASOrAKYm1eJAEyfV4kzz/BbHAc828br+sqbYMZYhqWXIl0hEDw3Hf0e4M1PTHKmkiwVIn0EQqURISXwn+EmcOMlFqharYirnzmMbFxLEGECKxVx2uTvCc7S7PWS0GhmdixPKFS77Wnki7OxNBulPQ8Mq5p95YYf0jF6DRDYMcy5YhqaBGmRxbaLwsz8nRhprV+BqWpgzZcS8xVMPH5VWVLuW4i+te+5+5EeGTpv15ANvP3/hqqETwuIdw2M7h/thuz4eA/jRgNVeIBsF6JMjSVwzUwEm686Y0dX56yLxi7ce3BkTtRGrGUUb0ae7rF0b8V7WshXPDKwJHhPxfMx2zCEmj5xJkRywYorH0jZIVwhLbmIr5kzWNzewG/ctDcEwxPqL57TyIIbd9OaSY60RYKRV5jlw4PEEAniLIDD4gPMuazjp24Tkm+Aqmy1KyfQucqC7ZAKS2sKqttC1avnqO29Y+9Sr2naaUbIfDDThr9Q2yx5PtJY4A5pk1zZO+mh8LdlV63yuzYr5YKa/pER8ZWEUDukj6Sf8cTt8I8TS6C7MhziteWw4YFWCipRESlp5CtfjgFMJhtj75gFRhmBhyMfeJjVxUnMObE0c8NJTngKrt1oU09PV+rX0vDUZxRq99dkCBJANXxQuAbehCOCgOUgie6xgoJh9OFe5xTEgRTcgVjQpl6LYyhVji6Ls2XiUYboYOE4KPwkn7x3u9XKIJWEU15jgpP8LL30wTM/eiBZRv72EVKzjKFtJap8aJFU3ARw1fSKJY8HxYckLW/5877ADJy8/6DVdcq7UuLrO6ZaWoTN22XvnKTO+/dm1V13dVl0jZDZfXI4jOEkEWQ3ifrLpCSzmVKIS2AWtsMVdvfjI6k9+mt2TX+P6gZyGwNhv+iUhO5phiCNvu8v3g43AIFcJxWUJK5J6hBkxqElLQWVmSVIlrmQxN5CZn7K6PFtsQx2bxrx7mX4NpmJWEHZT9cQdFZptHuKAB6QRGOqW4p/Mtxwr6jYlQUjAvodCMQbRP/166e03VKZ27NDSml0D9NTR0RHOPjtBFbY1IPfyvGQJtyEnza7ThvhE1CkAxOCkjwOFmdNwwBwGHK21XBj7aIYLFQC+fWxwszBjb4YmbSyQ/UFLYlB/OigaRr5ZccXmMT16QBjwVEj0cf2LE3D5dk5NnLSdT4Ca3WdfsVH2fe9L/AJbDomkJiJrkWfe9rFUBNQqljtv37vlVClZc6tzwxJ5xFnG1WKhRtADOlkcZvHN/RdBkvtaBfL9yc5CjxaJ5iD4gCTwaxD04/AyMUmvD5eCSlxjvw4v/Wo4c0nIC0UtFHsr1BeXYgokork2TuqkknzGhMXLw0gfIT7JNv5K61+e96q2MSN2XzjU5ekilTgWFiM8iIPPitY2bOFf7liThg4d8v++MgQcFmSRhUkf+lCyY42yigqn7Hqc57cFFDQgkl3uFtMUz0c/0vXEDU/+ecFOmfRFZVU0fM7IWr4DbBQVdAJz4xhLA/eNJAuMKynRXvSr8Bqrfy1ErjXrmLvi17RkeqCXDEUSJArg1Z2sXynqUdYXCXKuvMIbdBeJ0J8TJi/s9lSqgdeYqQCfTcJBYOu0DfEgPmul7cRVMHGgD2Ou/vgVN8QLDprMCKsgsDU7aSOqKG35kACCd8EPPGbJxA1sjZ23Xr34hUcMm7xHJqvsugapytS1xcxAi+HzMJrVEYn6iw+eGrdm/snhp3UVJDItBjalZ7IHkYeqUg8ye9VpLvkQHUCMAmr2111ISP56hZE1AAwHSylvUsWTbOTmgI7HaskQ7OW5U0bLbW41qWydQWC0AEqADFry6/ZMTZy0PY9+ed+la7MHJ8oUnmz2bXLcg7SFE50kYJNoGCjx4peKS/WQgLb9bwyK1nKiyi3IL4KVJCJJISTOIKIw4gYyzlJP+oBa5EKGpSzSqlWyeH5lGP2VcMYV4a92DY8vCONnh2leYZIrpoUaJrIEYvaq1LiJXYOJXeq2QAKrl4wPC54LkyaGhXuFB54IO80Ou0p6NHq38aN2SppK1mehJwSWeoUoMxEzzljOsrQBEPY6lexmhzJYFMVMsaf52WsmyHINioK/0Kb0hKU2Z0QKkuJ5Qy+n6KXfDFd+8YmvvevqLy94xZw+FrV+TDtp5dkDDfGSE1bDuKCPHlIPFJVmuC3TNUP0G6dGHH3ICVwDynVflkUrfqNfi1TOUCTuh5rECRF15hIYze8jbBzYMiWYSD/zmepbwjefF+75ZnjzsjBKFC/DnqAu3d+A5oFaD47GEyMp3K/Vl012XggRIAJQ/vM/JxtOzUOPlbVKqUUNypeMQ0IizeMyHldZMm7osqceWbV4/urJtVuMq6zd1pa8bynQN1jGHc9ZxTAEmC8lj7x1qcfKs+aRd0mKY9TgY0w947pgwjiwfDL6SkI2zL12A8BJjIuCtOxcs9aKpDbFcqSwRwdEZnBiiMIK5rrddkvuAtOrB+aLiAp68yTKwGTpoqrtmZo4aXse/ZK+e1zteYkgKf5s8UGgSy5XfORKrt/4pyymPbTZFpJKnFnsw40TMUR40fQEEAFRi8iIjUqaQdhF0iOy2yelNLYgPVM8UAP5mMrlYoH+zrTMDxN9ahWDQGIoA/aSpyAFYQ2T2H5YWBwL6Vi7Q5jT/YaNJBvkyjBqIq0xZMXOM0cKjKCTrG6ZJ3WZXpc0m1tT+7PkKydUJEYRATSW6RRMjFR13x7SYeDCpzEyyqxu8ERpDkl1tLQN2fWvXnDkgl0+/a4BGC4E+oiyj9gx2xBgHXgzLrnexcGl24S+iROisQSiQXpQEY3FxsbHau0umEa8C43ICRK3JqncLNVpuo2ZCsxKH0bFoCUGADDr3/89bUX1jeE7rww/Ozd84qGwGxwuttgmwe5AsdrTPb16Qxwwi4KJcV964/UZKYY0I2W8YAtWMZigzhPab80qhEHBI5Ye6l+4oXg7gMAtyoB+Ut8O4dlFgd2IJSmMrcydNmp+pdIycmzr0GFO1CNzWxaoT34yeUDc10C7hefaRxe4wyAPuKRIgIj5QCIp5umI08bfOOiqQoSeGeJXHwsqT5PugMvygXEsNs4i60nzRCIM8xO2i5A9hvPFtplRKbkp2Jebxumv289BEydtP2PdUE8tNTwYOWJTAZWI9ZQ8pZbLxIHFRwwnTH/a2AeQHIHCCARGMBqTFNSVPDSNE7FFt9HHolns66mlOxuvcIOUXB9hNDh7UiPNZsZnFcAxEpPeEhLrmJopggO1eRnZ2LBwVRh+UuU3L2//ZaXFUnrNTfu/9YbRJ8EB1vTS2PjL7MHXWepJzDWJSohvpwGwqDqX1/G65q5NvwIQLFVsMPRWPRo+vHOXXbrTMsVS/eMJIIbS/f73+5glaC+7I6n5OkTzwUxUI9Dp1TdWJqqiBRmZgEIfag+fnbzwwmRNT3UZBWp41qzEHFJUinHdn96xPaz463D51eF478KjbWWKtrExprZKy2zsAxAB4IMbGifDrcty2QOIWGTuCbTyaLN2pLiwwdqw13PtuYBBWWuYT4wLucF9bOoSGuqP+KNY4eOhg3dr5/DU+LB4yvjOVZ2tlaGVQ44bvUNHt9+reEHmDDAkQM7YmWxGLXrcCEkYVxAk/7L1Ro48VgCKwq7yMbietQiS4Bhd0E5w00cX2GL9yofoSfQs2Kc2UPHrRhrJWGuW4irk7UwkzyaRTqiaiprh1trjFu64nVMTJ23nE6Ch7ntaspqYrIlLNAZeqxkhpWw862HSaKgN2UIiDRGfoEWhBhA0AyIgjwQX9KNThCaRSiisD0wZ0N23rsKS63DoiKpJxTFeRbM/NuZIEsQRofO49hv/esQPn9/255Y1q6qtnYe8/Q1HdCTTg5WF5hMSxChVKyAsV2H61dqXxiXEQSV/GyfKQDRx/TxVeheJPqhUKqUzgX6ixT0IWdIqzwLTkZ9MJA0DBx00sl+M8gOSWNRM5nhH6xMJoMFHMSJQkfBbH+G3DE5qdmuB/0xHvIcUG62ZqjdN4tq75Zbepk0ICyaF51iS4FvvS+0Oqwf7+kd+vVVsiCNbOxvHSRjCOCcgmjssMgTWJGfYs0VTwaMxOqffdgFDgvQ93YIH4A+QwoqOvSQqe4+8GciPbNSwutS6032L9sfD9Ekt962qtkzYsfWY41uPOmlM+5j+Zx7Pl0AFAQDRduimMIeWa5XeMc0KtPKTr5EMIkcz/7JnCqRT3k9ORrTUPSGToTcfmFQjSDIB8ArWWR//qduZVzkSuiTHVQSpbg3p6o7mOd7OqYmTtvMJ0FD340Mbi1r7svxHIoC82Ur0ogAmDppNTBLODoKIYJqMJSmGgNBVxFDESf6iUjU5iBttG5fQKxSwAKksTiLr6S06PkcTKstOavnFW9svm9w6r2VtF/hQ6ejY6fj9dtqrp6ClqtrYDHKAI1dP6VcjZWOalDlW540QXYLYruC8OmPKVmGhHwkKsc6WfjpLFvEsqbCU4CHW1lxVzAbwzbnnJlDG/KEgu1NWZisoP1YPUOUTKc5AJ93Fzm06zAGFqgB1pXIgiRPK4p7Kp3opMLviDUqctJYKEFWaGXJBGCNH9G5h9m/D0Ryb3WrO/5saJ8UhKO0/ezAIbhqAztxGEACTD5iYgqR4FYYIRePJwpBSQ1qxcuBVLB1lHynuYzXxIswymkAt9GzDqYwbcFgZYRR2tU3fd8Tpb22ZvlPL8140fvwutfePZKowHCyXUKxU+xYSBssdTRKAzH3Nn46OBCqxdTljHBUw3zTPUpMjG96Fk7DFgMb4obiWA4nUqXKVOMN3ptoNTtojBtyIYJq7OJDywMx33+2cNgKzt3OObuXdF0diNZMTcNZeogUjWfDlSByJX4Uopqo0V2CL+krQcNhZU9JAVC8JhVLlRyKkx6XNtrQahI4vrWpwJ+u0MC5DY/M2YCMplYgp0wYT9LCygA8RxykNHVoZM6btoeX7XbHstB1XPHVE2/VTO0KFD4lmEA/bmfDcjnSqgq+zPpPTOnMHMBZQAs3UEdxuRN+4l7ZhQq3sU1bn8i9Iw8PeIO4qEjeEynNkKW9PmbnNeCkRebHlQneZx972tsSMpIXCawZKsTtxZKEiXKKxzE9/ac1oZjBXmZeAJzCRBqX1BbXY/K8jxt2I+GrnIPWGROj/MLzu6PB/l4Y3rEnsSfKkR13XuMar8lNNCvMOCXfdEZ43O4ltH7BVQTBQKXE4Ehr8lQaIrwfrSBh4SMqPInvVwPCD80aqX5MSW4tQxRQkpXdPcZL63RTi9JFlm0uu9I4RU84fM+PUD6R1NHogVoyJ3c5KgwXdWpUBTO7CTeamWkIEMSxxz4E+zsvgatISLCA72AQvKg8nEcWaaomimI/pF2eI8zYSQpYbnNSvVYZDm00kbTDtm8YkfG7ipA0+2bbuCu2ttr4hlVKy0GG5sX6NlFqM0wIOmLj5v7YKnEQE0J3ULdmkL1kp6Tj7NdvBeEyOUKUkXf1ixQs3yBl3J2pJTIvR0gaQaFSmFsYyOrhBCAgmMbNkLcvuQhkwLuKGm/LAUudPPjnm22tO+nb1pZM6F72w85GX79t6+H77z6y0eTudVf7731u99YblY8IiL2oT4j0I84YO+uBDLYKNWLlijnJ6BQfAi1I67bRkxlJXTDWRABTGDEAnR7w/9LqFteeC3ioSG0DEi8yrg4OAudGEkKhSFGeaLvuaRtFRnOatn4AzcJAZSfQ38DRrVuK08rrA2OUrwitWh2HdlospoNIayYBq25OwFARJHan2V00JT08MCzrC7PeH/2kLrVeGV/53OLNODUW2qNPmjyJ5cYqNk3gVyQtwRJ6ZPB5MQ1aLDI1+9YuTWOBEdJVSZCnWQZliwiAYZkJsZEGpRd4BNzhyC3limZEsyTTJtOHnAoM8m8Za3xVgITOyvhKb5o9iMAr0A6BEI6IzJrBLmMfYk+BIUksZ5jcQ30O3MciouaOmNinLgSZOynKjeZwEkdiowiYs3pBEtrZ4y1sSA2z9x5KAi2JoC+cgqSRKgFWZ4hfeO1Ak4fKoujZGN0ko9dci/PeOC+qQF6kWkbBGgSYgZ0s1eq0L65wnoL0KzTo4RwwYdiBz9DCi0AEkfnfmRhDGRuThs8PY2V07/+baMO2+FoEU3jv7nx9f+MIbvvXhcP3wsOyxsMuF4cxbw74SL+Sqrf817oTi4cWrIsErUgqxqVAn9Ul3hGLklIHZDvb5myNcFT7CRJGma88VMGpx8rNj9Xvr3LWlX1WYnZm5oYRm8AEH8JxJTwICGFQMFszHpgv/maK8WitWjN19pxPfdnrX0gUQUkz+Wc8gBNNwRusm6+q4sFCqiDVhyOHh1gPCzZPD/OeFG28Pe/0mHFva4OJJbdOY7Ev00jI252twlkxX0fr1BQjvj2nWL+EbvFtK8Tx7IWDkUYp+K8JNdLz8EoLHiwTrDI5E41lIMEPasBnXEu4bQV4c3Dh8KnfgoQaCwXHgSfudgYSUhwsJXvsfoTqtZZwzFYEYuGpjON0G19Pt5KomTtpOBnoA3YxhrbLaENAsE/2u4VRte3NMTTaA22ymomR01DRZVdR4W+pAmcYrSUtCIQSfhSMPC9lHVhbrh5DY83hYmPR4saCBQpnqfuEvkhXNCxP+VH2+oIhaRpT0vg0eMF9RwDYcFVGyM4JhLX8Z5yya+Tu69Rygl2A9MIajZ+786tz5nXfd1frMQ8tPveYDx4WfjQhLBYMdFNrXhJZnwz/K1dJgS2IxeiKmVcS0IpmxQvvBxH7poIOScDrTmzqPhGNi7LIJG7OVmDDgdS2i2OImBhirvrKvVcNAz8cJ4F5couYMkIH/MlXalGoB4FehPP4OGTL8Oz9JwoqVieQhLZs/iW6mhmEs4ObHP+wat3ox3hwbrpeDdGJIMlWASueHDx4U/txvU1kWIWMIQ0Qw6VEkpqPCBE6Qk2fBcORAYbzc06F5CvRL/Fa2yNkokCMDZHw9YjAHfJzOHwcyfYOYvF2C/bME4qTFsucbPIbqRBHhhjWnUHRd0DsdB9dIVAw3meO08RPgzlCkJOumZBdipzo6kvt4yvwan75GYGKDbWsWGzAHqtsWLV++/LDDDnvkkUe2rW5tQb054oioC/v8/b//24JaWKcp3sX6b/9WHTGiOnJktZU3KFHqm/Oz997VN7yh6m97e7WtLWnYpEnVFov/kPwdOjQ5/6pXVZ99NunT975XHTasOmRI1ZastNm28f5r+Oit4eC/hH1uCwd8M5xxeBCH3J1TsL+uxRulVWUPpk+vfvKT1fvvr+JYfVJg7drqWWdpkjfgchd18tr4JCn6QuewIF1M11vCJQvC2IVh9NNhh2fCZMfPhQlvD9+oBGHMvX1p5Fj3L7usvEVnn91oVWPGVE87rXr33b313HBDwthGGlAss+++1V/8Iqnq/POrHR0DrsSYGvpitQ2eMWdGj07u+4EPVB9/vLdH6dHy5dUf/7j67ndXr7qq6vjII0vuNXly9c9/Tq4wmvfctPDnba+6Pexv35VplH7kbe63SfryhS8kldSZNm96U0kDTMU6s3Hq1OqnP11dsCBp4Zw51SuvrH7/+9X77ksmXpHIomI799mnn1Zp8De+0fNkjR9ffeyxYsWDPHPXXclkmzmzesABVc046qhkpF7/+ur++1df9KLqIYckP73rXdUlS5IeGcE1awZ5o63lMn03QCefvJnbu3jx4vb29iX43gBBuNsUNXHSgIbzmWeq73hHIqYpiZNOql5/fXXVqv4ryEIlOruW3uq/ok1egjT82McSkES7bAk4abfdEqH/wAPV//3f6j//c/Waa6pG5HWvq8rjAzABKyecUL311mpnZ8Kp44/vg5C6lUHn+8MFd4Z9fx1m/Ti8Qr6cm8MLLgzv6wiPFlVF8cyECYmMLipp4Ozee6vz5iVqtY7C6x29zs5LPnBbBiR5myvAlMCmSeEZaOmycIpG+jwRpnVDpUkLwug7wr5Tk19LtFr9k/RNqS750IeK/Cmv3KTdYYfqV79aXbaspxONY6xc20ykf/qnHoX9618nmm9A88pU9DEEA7oqbQOrxF7h0Xe0XvIPYy4454Bf/PqK5zrXlGGH3qGq3nRTdezYPmzx7F98cebBN/CAybp7pDhJjvV159IfE4iWonbHH/1oOXbJ3L/64Q+XQFKLgZe+tLfa7I3iggF/YMFXvCKBF9CGz+GHVz//+eoTT5RMBs+UZ8f09pk2rfqCF2TvvxmOZ8+uXnRR9T3vqX7qU9Ubb0xYDTyZNrDCKadUL7ywunTpZmjVJr7lihXVM8/snS2G2MxxxvnNQgPCSU2/24AtcNvMBQJ0GOrTiApOBzt15R3x7qz6/m+5mCXViFGfPC+CEB03YhXfEljHVaGpMbhyE7SHp8PtuBWKvgZ3Z/C3e4UPKJtVQUIdMZviWxntY2xQdA8Jq8pVIiZ6VvjtkjBmaRIWzTgzdGkYPTPcsUd44tGwS79Rt24tJEWQsqgUIS/M+9xP9unYe298hUEIEOaL4QKIDgK3UEwsBd+WwiJhRVQkPz300Jt+8eYLwjf/GPan67op8bwJRRoVls0Nk0aFpcvCyHlh4oowfFp4cghrWQgPhxlLw4ie4gP5z+4n/OH1y9GsWUmW8EYIG7mi8FN3+DVQ6plq5PK0jAh3+/44vHBDVJBnQahN4/5czhcPGkeMA5+BEqPdSeFXHwj/PqFzUVgSOu9s7bpw1txp75x06B6lr2uN9csm4B2I5pgJZhy5e7h0RalrfA/xNtkGJmdz92yL7YKWvhnetq5Ez//aLGzc5JHlkotNaivJ0/slwWoclGZdyiiOMNPMtq/S9PdawUXlrxh5Hxzr6Ejcc4ZPvgAiSzy+oc+GB0g86yPIHdkll87eftu2kQoIM8olMZHf4dxzkzmjOxxqgxj9jdTUjVet7Y3esdM9p3pu4ljaDl5afsYtnJo4aQsfoI3YvA9/uBckxduAO3bVCm6gv2uRfd2kUkpErWxynnMb5aLKSX/aMg+gFmiAeBLekX1oN0ZrIQm5cMRMYDWoVCTi0r7oHIkKAlN8skRDFN4TEsaEpbbfrQjtaUlvDR8eVowJK+RzrvYXJS2WGVTSNpoeN9QPf4jntSMpkuwy4nK8fUJUB2kutIV6k8TIhh1slEJJqhhb69u++72WJ2Z/fsTHPrX849eFAzsTB43XiCa7ykeGpRTtH8Nhrw5XSji8MIyzrWrH8Aw4dU145dLQQGhu2rd1B3Tkv/xLMlFzifIExNgbb7NbI6S/kEGM/FDerjFRPqXBMaW1gbCwhf1leIgoctFR9m1JDdU4mX4+pqKQncZvndY/PTz9/nD+6LD0qTCl0tIyIiwff8Nv531vxqjdp4zYsV6yH8ACoBHwLiAG09KUHz01w0lCnQWdyVgP96GhQ1e9/DWf+Nmnut94lt4/ed7FMkKKEcf3/lD7CKNUL1RIHJUVCxKRI1QZxoqwpnhp+pDGAzXIj+COxo7YufbaZCeBsCphPfYWZEmA9pZM2u8h2n7IAswSK0c2OaZJv3I/bVFfmzhpixqOTdoYb58oEgMD6VkHJ9kHVyQawnuCZBDY8imuO0nYTbCGIwdte2FSYqKTOiFdQEcuyXwjxhaK6pdoCCn4iiTK54mw84Hh5hVhhMBbG+ZYNOaHSc+E8d54qrw+wmqMZ6UUhzKqHAgpBjLnGima1arX+ApKtePaS9liAZAaYLLP0bgffPWzM5dWDxl2y+daz76s64w7qnvNCeNHhGX2s98fZgjovjycsne4f9fwkBdo2KDO6HV7eNGvwrFAQmnD+j1p059XpHn5STZSmNqWydraNJcosrQ2hb28IjVC0LK0tc6mWrn0qvQkkxugltpgYhQwi8hACVwb9FQ8INwBbDwZpnrNcKUrLK+MHLl25Zyf3Ln46EUHSh7dC55LGgX15iwcfQqxMjE6WeyDxtonhdTr33zQwmHyG8FzscEYxdhpW1aDIAkqcjnLkxowH/L2aEC6aovErgB1wUCNkEoisjQbGcYkGnDhmWcmuL9JTQ5scA4MUk5t8HY0K9z0HIiCZqD3LfVQ0Jok/lZBjPCgg9ZaxG9sohUAC/Y2iITG8bIUq+FI1tM2r+WMRrXao6ny8hVpVRj5/fC6GeHeiWFeNzCqcr1dH467J+wWnW5sFfCERDuaUSQLcUYRZEkni7RblKIEybRcDgRffHEfqKewSyTTe8nS1724Om3U8gUHVv743rb/eLBz7593Hntn2O/hsPvTwRq/5S9h5hfC2ceEG6aHR1aG4Y+HPa4Lh93fgGew2OZ4xq3tTrJTyeamLNkSKHlg7mS2QDymm/ll2JBSMAEYcOLAgmwoTHfx0WB3rEV8f9Qzt3UkBjbmkFLu1aohnodBNcaczMHT+lfFX5kM2e1iAkm39qWzGp57Yu0Pv171OmIgpkEEU3IvbeIJ9rqQ2KxWRsoEK4MjTIzOsYXouwlsT3sjxNFmgnH2sR9EAr0sHmzLNwNjO5knTUh3sU5rnGIDgTCWTuDvYx/rBV6NV9Is2eRAfQ40cVJ9/mzgX7/+9SV/uKFVqjyr6osuat3AtQ+wOpIu5sfLXmdVZ7W3DZM1KxMORSgL3OCQYuPMIcQhCXfhn+Le4uMQBEZV0Mp0uSQuqUGifp1qyBpOsoX/MPTI3x728Um3XDt+5aOLw7hbw4uuCbMWJG87Twi44WGh20pxEhcVbynoxltUmmk6VuIv74ZPkTQMRP5ZePE14aCJ1SXPr97312uvPHzIDUurk28OL5rXNWad76/lD+HgO8OeE8JCM39uGL92UB63bAOoRphGYmLaMUv9er5oZV4eGCDrtqP4o3lDSJYeiVuy057rqVbAk7SH4K9MY7ESAx09VNmWNHgM4qToucFLYrG7w/MWh/H27c8NE8HiodWVrZXOuRP2uuPhMZXLklSEDc6uejfFl3Vk9gpZAxA5ULgd+VvBykZ2zmMOEMnUl8WdbJwcf54FVQkhUga3IVQHAyWX8P57nFUo81AxcG2gFTbLbwwOsN3GaIds5aaoVeuWT02ctInG6OqrOz/3ua477hj23HMMzcln5ZLV3/quSI7NRmIwSbqsfLeu9RrqnJs/1z6pPtLQ7/QnJ1275ZN1MiAoKkiCPvrJ10HYAAbUTVG6sqcw2EBF4BFcYqEOiXJYNK7GaCNeIYqkSGe8e/jp737Jimf3//A7l992b9vcMG51kumxx5khGJemgwxKexrNIZS05X5xTNftOYrTtXjn3jPA0Kow+qkkUnvCgq7xS4dNXDOhY8WccUmU1Lq4blalZWGsT7zMbOF2BLOY3LLqs7fS/o4MHCjj2hxO4jurT9JjQj88yxkMkFyBS6axD6J6PRcq95rbe+9NzhRJ0DHXm8yWSMkBWUFytZWOTq5M8esjYdevh785M/zv9PBkHPF7W19wx9TjKsNHwyXQnij7OgQLcl9ed10SJ6TvHR1JQDewqDGlpIxfOSvZwPDcnKxVMnc55oh48zdHRpAnjnMPTuI1k/vKUwnxDIKMl4+NKaCSsCQRfk3a0jhw+ulBpIeUnlliRBTfthXQZtmSt/FuusXmBfj61xO7ODt5+qlU1r7p1OUbjxWN1CxNyKGH9uzVlAbmiiv636Vp4/o6Ddp7YCNuaS6TRtrQYBn1a63MBbasF++1enX13HOrumCj+9//fdVG3L/92579w29/e5KYJCXXShVj771cL3VSthT7WHrGnmq7jiVBSXdHlxZz0sZmd7Th32Zg++0HSlIZyXyTu4uuxUQvEgf89rdJsoPs3V/72p7RfOih6mGH9fkptmfRoqQVK1cmOW/61mxL/5qdw6MHh1v2DXeND/O69/nna8jeq/s4meEuHBkWjx6yUmdLMxJJCuVz+unJOJpLL395v9WWF9DgM85IGp8juWoKDes9Y8Tf//7cFeVfbd7++MeT7Ax1ajv44ORaeZhsa69TrP5POoJRg8sL0BpWHBVu/KfwL+eFD50VvnZg+4MH7b/GhnlTJWYbKu9bNUn6YB5KEIAhGhDbcOCB1euuq3VFvfNy0MiKtN9+yceT5SHNppN47rnqrFnl/HFfcxirv/WtJLHQscf2w/D6nDSvPIzu3qQtkwPz5yd5HKRsMI5STzg2DzcXDSgvgPX0NkVbJk669ppVLcHrMXtBUjw+4ojuxDibdQSo2PSTlW51GiXbR5pgRU62H/2oj1isc+EgftK2229PkjHutVcfXf6d7/RW9vvfJ8lU6shQaVTuvLO3PKj0859XKTnnSeo6F/b7E4AIov30pwOT7yee2NuYxo+Ilf/4j6rkVfJS/t3fVW++uQ9KMHb6SEsZEWmZ/vM/k8x1kfxE/02Z0ttTWjlNDSqJZS7z0LCwvD2sECO8V7h39/DAkeF6b1Qdauv5QHIdyQk0Y0bC4exVMSki9fyzn/WA3fqwJntt7li2HvCuSE8+WW9MIVrCul96+ulEZ+fuWPy6554J5DUHZJwq/trIGVzyKEnmJItSI+VrlIFru9qHdo0ZnaQL8qR85CMlWYWyvZbnqbQqz5H53AhBqPC3BcnChfnu68611/bKBOLCGqb0dlCarEKU5Wc+k0zsl7wkSchUWrKRk66VMPNzn2uk+c0ym40DqbrZbC3ovnETJ21x+bivuYYlKcm8l/sccUR/2Y4371TarHf3OIECdXCMrIxImrLzzqsjW5Nshz7v+JuuxYt7+/Pww0mKM5DCGrTOLfqVzo8+mtQp7SHgJfddv+VjAegha+KKNbBy/eM/Jtn23vjG6qWXVq3CI2zVQWr7d79LMgTOnZvJB9jbm4aOJDKmQmQTlegvYgV4kVEH5MKETMu7Zoa/7BIenRnuPiZc++5w4a/CcT5HjHxgHaOi3QhXBaWb2Nlr+xxfcEHSTSySHpACizkVKXINeOqpnjbfdttg+A/nSWFcapaL5jEjG1u7rs1JwyCSf/3X/nnFynXqqX06UquPOgVwgIO1CpSexwHNA62kBd9558QCGlNNsioN2sCpmzCoekwtdkTDWmvN4zxoUtowJzUsJuauwybPkdTVLDdwiS4U85Sqx8IGagehkGkmA2fpHXUfymTXlMocJyG8QZvW1M+Eqf3ms/ShuslMpana4Cth0qQmB7IcGBBOasYnbRrfKDFBuyRbtZvUIAfE9Ng8XCd+yHYbZST4sT+8L+F2D0FIE8NzI8Kqn1487iPnjB69d8+EF34rYleAs71gQnPq3GVdTSX/i3OK4SziE088MUkYaLuNJvVbmwgP76VPMycpz3MvJ5C/ooXEzdhEJsmNt+YJCrFLThyMN5si0SF///fhFa8YTKy9HXaCl31S0lQbjnK76FkmloeRi8KY3cLsl4Vr9wl3TgpzxlSWHr3XY4tX7PaXeyVrWjE+LJGlSYakZ8NEr++s9TrbW29N3vOFRVLMXX55Eoyid2JcxFppTCQ71AQoeElcNk6ufryOX3Hb/ilsL5ItbPhmt5csBvYYin8XxoThYlZs15IbrBYZBSNoO6cXl9Z52XB6uWYIMvO3tBlpsdyB8lI8iMoSFy8Wx0a5XPyTERe21TipEAlFkn5CjJQoZhmwxMDF88V63FpCh1qEVzGXowICb3UwF6ltmARm4aSoJh33tTQI3W5EI+7NgOK3JI8Q814k81x6BREqop2kocJz3BBmNGgyfDJxaJLYLB8pKEVfmQOiMD0ynp3GIwIH3YbmhdskB8okzTbZ0c3aKYboww5be9NNOW5XW5Pl+NYQ/7w5uPelL/UDOASi0mp2HvUNoe0FSVotQ8/cMGVymDO985HL/2vcRy7cnR5C/noZqp3JZKv8itQDcV8L31A5xZ+cJNyz2RDsvdIe6YbTzc+12EaXy0GcEgxkP5pEhZHUKSGbTdQkPgwHJ9kcRJdQSzL0eLG5bUcNJhRIb1F6IKUT7uVIdPszYcexYcnsMP2K8Dp5uUeHJcPCmucd2LbrIeHz73m8q7p6SnhWGgJcGRZWdoYh9tmVrgFgoLh3Un8lXPYqU8jJdnqwSQg5wES7+8lOKEHEN9+c9M5QUnWRsTKaFtmutRSe1Jd10AkFbLdXigawkfbFwFrQQZ0xopyqBl8g79LkFzlGaTy0rU4x6Ua8LBA+d0XyVY8ENcM09sBDJNqWIy0xORUr7XuusJ5qgA8+2zcEedtZuffe+RD17FU2CUKNtchN4XU7BtRsR9s735lP8YUzIuUF/scJWQfW+MlAiygHlG+/veSGkCvgYkOoyGsfO0DFeqt5QDAxW6+5pP2eDs+yW/tIqIGZ1gMwuowD0HOTmhwYBAdymnsQNTQv6Z8DBxww5KyzWm+6yTrR6q97ARjCiKFr33x6H6Xef0XbUwkWl/oEJ331K9XzPkc4RpbWYmZlfhg7Miy/4ssLDj9h/jEnT4jVyuDCLEEuWx8T0CRsBD1F/eQMteFv/MmxzALUJEuA7fSWqpFIZDvaZJTpFydZ9FPkKVl8wwQ5ApXOO6/3HIlPr1CHdgbZFmT3HK2wngSZlemkSntYtWt4FIj3Nprbw4GHhpvnVqd+/5ZdTzmqetaOV37lmZcvCmPbw2qIam1o3SM8xgqwIHn5V8/Ejq3q6EgaGdMZ4BvzA+ALiACmvtKy9p3NmhVgKfsrP/WpBDRgAkSomyAUZhodeStScBOZT7N+/ONJ6qPGqRErggkAroFWMESDW65UG9umj15MAeHFFvbbMIYW5Npa5f2kTs2oj91VYr5Fcw6cAXpiOBNs/f5CLYBdEZ+lzY7QWRtYN/Gf5U+Wc6SDsCn+gB1Gx1OAHNQn0xUiLO0pyPXVrybrAbms2MD8Zb5ifILSSsvXv5FfPRGsX6xHbGbmti5Eg59J7pEEm2Qez5nH+q2zWaDJARxo4qRNNA28VunCCyu2ddz4+1XVsPqCC8aMG1U5/a2bMy/AJur5YG/Dd2AVWIfGj+367L+FBYuo51oIqedqb9LgJFoSRl30D4+kOIkYZbfgvDM03/lOosLpDwIaWsqJaSWzioEit6fdspUWsas5xUluxhYCfimfqyHbCyCApnevlAjxfnWzCrkS4DPN40qg19cfJ9VQqJUVYejyMGpsWOClKM+F8b8PRz4Rpt97X8t3vrb8S+OuuveZcb8Ph3a/Ua4yOiw+PnESzroxQJ9RZxoLsUEtJ5xQgR6wAlFUAOV3v5vY7RBLA9jH6WmTMB0sw5PzTE0gFO0roQ6MpafsRjic2o1U4kK/Alix2qSuDURmGucm5N14zYAsTc+KY26ANckMG8ie9jozxE+UvZZgEVziwJlYPjYvohOcwSWIUzJxWdFxm5kqwpc6XIHRsRfWqUWqjXfBcJZOWS3MTzZXY6GPbg2FGAjtUSy2pFZV8bx66pCqGNikBpBgzGSQm8CNGOecHxBpjFHwYLITa610Sr7GjmikBxMQN82aOGlAXG0Wjhxo4qRNNBP22afiYzvJE7OH0CSvfJU3MmVU5SZqxWa7Db0OUhD6Fr5iRBoRr96rIMduVA/FdhPZ7cvmz15UTBGDu93Kuc81BGa1M7Qsnd/Hz0GpcFLstluyFqfzxCqBL5pKi0fNFOWsYqR2dNw4owyMogs0CmWZJUksGYrk4iOmiy13rdW5vM8W/YOgaldnx8L7ZlbvG/XLcOGK/eaP2r2lrZUDTm1A2yCIcuUQiVa07OVrw/D7Q8foMHF4WLs6tH07TF0ahk9cs/BPt7WGfUefEr63c3j8qTBtSOicGe7zNpJrwwk43hZWim06Mtx2WvjhzS2Hjm19WaXSk+4S9yBRL/fIKT9OGQobu1j1HPsVi+h7MSs8a2KbmPqcjLqfuvUrPZoip2yb1+eYWpVoCgxFxVFz0n2L552hes0KLYSQmPoGhJPqNFhPYXfBPTxc3kttZppsEb47cEfD7e5QvmJgN2QQc57BAaywDD+QU60pAdhdcEESIlZKeuqDtMEtfGBZH/PccaRYQK91uT4G6rmgv/9UBUZzW+sLEGzQ4TOLAZX7qV/SHjPErNBlKNATzShFPmTbxn6Gb02Q1C8zmwVKOdDESaVs2VgnDzqoctBB4NH2gpDIVmHC3BkgiMAUPgL5/WRDFkVR5xVykfveSuHCt761fCzIxDnP9SYLTgt1a7Q8TmoNq7xZjLfh6DdOS0umB0Qq9Pa+9yXx4PQQmfvoowlailKVjUEvrE35OKx0qau4qiaFwalcXDAlJP7aT96MHaES3clMglRr9a/jnE058lMDVD0tfO/Uriut5K9/4NCVD94/M8yZV5lw2Q+OPq/jyE99umUQsReCuKETWrCojeTLXhDaF/YoR/yUQX6YN7bNm/X6Hf/4uWO6rpkTdmBA2SHMuT4c+3CYirdtYc3YsHjP8OBrwv97SedV983vqqx+dejOuw0fc+IU76LXmCMteKqDHYBT3DEu8Qphrje/RiRHHeK2+bBhcZLB+p//SWJ+61DavFwZ17J8mA+YD7IwveRwc658419NLfoefNdrfjrTj0VEHkV+SdMS2va6D4n7PES+ytBoviGTzRSFCUD/jo5wySXlN3QtJFpMyOkWwKJhyo6UCnMUueFZdjvNM3uz5XOFG/zKZAUOIl32mjZITkt0BIdrMT+tWQN8RJ3D/Z4vDPGo8ufKzG5JY7Z4hE0zvlod3Ibpi19MYhmzZAVV2OOS/b153CgHmjipUU41yw2CA3SwlTqolEpSSz2YCWJ417v6+J6KlVM/gn+hJduXRNfmSD0jSuxGobUbyfQtXB2VJK+q7jRi8VmfnNn3p95vhCxJba8TfEO80kxe+sHFJlrCeQCIQ41SpKgIZZLXRyiSFmaJ2uAu5FY744ykHrEgFB5TitBgoRLWyl58axWeI0rL+0xEctShoWHp34QvtoW1s8O0w8OtHdVHvdFrSLXryId/deHD73r3u08VfqHBAyLqBERgt6Bo4ZiCYUlqqV7E6c1xvnz12ZOPOWHlI1ff+6fqC54NO/K03RP2nBsmjAuLJofnNG/3cL/Xsu4YZk996lvtyw78wQ/2vvjihBVRC5Y2r6gInZEIGw//4R+S5Ok0OpWJS297W6L/MHkDEranUGyg1Z57boKwKWbeQDOcUQcnWadSMta4qvEDpbvvThAP26SdYrjBtQk3cKuJhYez2V04mExOsJ7pSLxaxOLpXSAqyI9JRpR0kUxgufhZoVieUgK5WHDtJyhMg7RInwOPp9GxYU34kXcPryeJHIqzl9WHHVHHtZA/Thd0vzROyzSAgTyqEBJ5YqupBZhjFF/Xo3dEje6ok3Pco6rj2zAZCKa4LNnuhz82JzZpfTmQzSiwDRxvmXkmtwHGDqILEudIBZRNYNMdSMR8n+Rc+eEPG61SPSec0KeedbAJYXIAAEAASURBVHUyyot96PPpCA913yU92TkyzHtBuOWUjlueebyQvLlvEyR6kddHijyZMyX1lohSbhtJinlL5Sd0cMwx1VNOSVIcyXf8gQ9U77mn7/V9v6UJbNK8aumZvgWTXC+XXJKkfkn5U3bQ1R4WfyB8+p/Cp28Kh1wVTvhxeKXPjeHQ34Ujp7c8JzGSbMhSOEpC87//25O6Jnej0q/uroWf/WwufXbKwJ4DNjlJFqTn+atXdbWG1SPCsqHBm4QNQXV8eEbm7hnh/pPDD28JBz8TdljUMmbNtF3ePvX360aqftdKfpVHR3b1mA5bJVIfybok+XgtHpZ2rd+TavvEJ/pMrTLOlzQvFpNMPJuYx/EDD1S//e3qZZdVpfHUfhl9TPVBZEUyx9I8RqqVhurqq5MzcgJlOSCpZp0Gy28kD2QtkkLMfJY6Syan17ymKlmrlssXX6fC7E8G6Kqrksb4eMyzPw30WDYsaVFzJA/WL3+ZZPv0XMg4ZWJravxgqWRRzuugGSIZukSvcphlSaskCfMsX3558pxmhylbbJs5loCqlO3vfe8208UN3JFm/qT1xY7b5/UsH9asVm9Wirxa1sdxxebAecGbTCliR9iubfVqhMS+3HRTudncAlFtlnqN+FDsvhbbxNLzoQ8lZglr6HXWqbxhoRJWLw4jxV6cemrFPnNG+5NOqrz85ROGDevZ41a/2ZabTAIx1IPHx/s+xc0wunR0JKGslqdsQlau3A1qjiFKdSpMzR4aXJ+UlEPI+p6/STSx+ktpVRh1WXjreeHDtuKvWee6nRcm7RYe3b/r5p///CRXqUo73/veZH+ZEHWmDgtKA1pnJe0S46uD9Q0J3d6VIaxoP/4JtrctX9cAN10axu4Q7uIie23lyo6WJ4badlQJTz7X9tiaFsJ7cKQx6fvszEn+LAkRWEeErGFUyttalRsgJhb9YsvRfdbBaGnIlWfGk4dJ/YMjZh57Elm5MNlMNi09GvZtYTuymjdvWYYEKQsjO+uscNVVDd1HVcoL5Y5k/tjwn2bbylZRP9BN96XLkpuqlPjmYioKUU2f/Wzi4jRzGmeFGaVhcSC+/e1kV78Ng2pg6DJYrFmNV4VjxZd8eb6EdXM+itASvaROrnB3xGoPCEOdh5ExT7ya/AWR4dluahgbMHNXv2Sq8D5z2NlJgNxCBgE2bNa7rYj0okkbjwNNv9vG4+1WUzPUwshvgy6ru2NRIKQ/9wFIxIINIX35wq5f/nRVZ3VtZ2XYHnu3XXFF4g3plyiqWirftYIn+K04LOgwQIR4qkOEppIkF5mekQgUdh8Vx/Vz4LFTqFIBHOKE1oeIY+rNJ1KKn3zVmA1I1IlOAZT2xkMGhDuIU1vHVEQzZwPVxWM509W9cTWqLtcCGVAFnIfoEsBRR+o0mz8RIqxL1TVBt1tMjyJ1VYZeOPETuyz5c8voERoHL3SuDreuPfzR0P1S2eIFgz0Dc3iF7UUXlejFbJVmiCAnaQjgTrylRDmGBIYD3FkCa1RVxyGYLVx6bHqrlvsJdMNnN4UjuValMAVrTFeerJSUBNrSV73WHuLkpfezZiXwrl8ycPWpv2FNmsRXCOLoi2dw3Qqka0xYvHOYK+fl3DD6meAtd/mQSnNMDJnyEYJzW/tEAmve//4kG1YjBNCYqLI6udwiJEdWKeeck6Aow+SOnKSkCqFEdAC+HvMslzRGMaPvp6J3O1dz+tUlUCnXcCSXc6fG8Lg6q4v08ubB9sCBJk7aHka5nz6KZmB7IKoiCSc65phkpehDDI0JyzpWPvDG8JfRYdGT1V1vuXefE0/c7Q9/6D8Qm/GJmMuGa2TbQZhadFo6UzCWs+5YHyqpzUZxIrWvgukDldaMn37hVxLpuYUTW5qtUtbHpDlAxuogebQVM2IDcIZKyMDB3t4sDCPuCgfvER6UDpuhpDXQZ4ueCtPvCAlupSfW6blEnbDAYal8B8wG1sdCW7BO/XhOhWdhE0BcUFEJ4MnA0GQ2TNkxMTuVUEtl9Pve3HrFkiAAHg0durDjwF8/e9ojc0ui5ksuH8gp3aHYivaDbB2wEcXvE6cKPsNMQAw+63tKmNwvzkgL1zkwUkwR0RrhkWHItAZgxRFzk6Xdpq08+y2L2hdV5nWOnzClzaCUriI6OsJ73tMnvVa2ktyxhEmlEUhpMaE59UkKbDkRtARnNF5fuDe8yfi14WdvChcPCWseCPv8d/i7u4LAvj7RPUraqwhPFIOjzTTWKbFBpR3MtccYQVS6bNRY5oq1eRwA3Ihx47YJkxysAdl9UpxkdSHzk0BGdnE7AXGGwSz9NXfT7FfRaSlISs9bKMq32cRJKUO284OM2NjOObG9dp9YoT9SkJSygfzyWbtyxeHhd28Ol+wUHqE55Qq+Mxx8/hN/f8klewicrE/0BPQDdZXGscb6/aSA5b5NPUceWa8+W+7Z2O1hIdn7UtToyTnysaDv+5bd3N9oFzHd3D2kMwS5006Jp0anUkmNLXhCaUX+ZNqrm9UVYfQ14aUzwkMzw+2SG7HBSfD4nfCWp0M5InE7NbsXo6B4bWrDVyDD0Ij/FWYeSUQwA6G//CZ9qUJ3Ou/DA2LZLUS9qPz23LPS9p53hAOnJT0BPSZNWr3bkQu+d0jXDzao5a27ZcBNigX7NrX3mz6yJGGgxiMHiM+L0S47xyjg+tA8uWyARLO6HTTPNCt9aGzAmlVdD//2obu/csuzv7j35Wsq1Rc8f+gLXzhkyM6WCkWgxotkh3yD5C51cJLeZdOZltZpErLSIVyNU87LSKaF58aFBdKNYuEB4dbXhx88EN6/KvTZLaawxhuOIum1vXiAqQ2ADGwmYb8Evov3Nzq6H5lW6xJ4yHwGiUA0VlKGN3/NSVvkbPiKk9M8N73Z+azB+h1ixYqkd+zrZghDMnPslg+YhPb7RLCedgdSbMTzmJZvHtTiQBMn1eLM9nLe6lyIRi3aLTzz2nD5zuHhhWGcaKL2sPIwsKk66ZYbP55bXxZroI9FLUhabd9KqaxM1RgrhbicrA4r1kZU0dMkF9uAyA+CrEgcFv0uoItXbbIzssLYxvWNbyTiLLY/Z57BkHi+BhSwAa31z2HmF8J7jwh/nBqeWhZG3xb2/U2QjqkXLJZ2h0ePOjQifBZADw3NoGUvVbQq0SX2irMBQKvZkaJv7DLjSGKiA+ls5aOVXZsl16pnxKjWpJwRWrFiRVf7kw8Nr4xMtEuNjmQrGNgxPWrjYX1iZmBDQpGZkauOqe3sHNPy+nap7F0U1n1av7hPPlvMsV7joWmvGY5x/v5rHh5y0dcm3fv7HUKncVp7w3V33H/C0W9/++tel/iIGZZiO11rMfC3f9u/ak/vyMIKubJ8lBIcDK/UIbvJoHbmZA1I2zAuLAbB28PyhSHJyjUkjJwVrrk0nHZ/X5zkJ/VnrZLZG8EuH/lIki5Bqkx2TTCoXxLOCFpB8JhWJKjImHr8OZQxzVcjYjlhNtrPZasXZIzhBtRwW2lY+JnqcddqaYXxFhBtKdTzqw25qKMjSXhmC96ABAtmmiqsq/6y3TKGeZQ2KhF9//VfiVlOrFUko0Ow22PbpPXnQBMnrT8Pt+4aaDLGhhrUuUt4ZvfwwLIwkpdHmRVhhOPDKzfOnywyoj991Z0NRSSpdXwt75s6oxqr3YbeptHWTEoMGxaOcFUq2WMJAUwWkQMSZ71Vb/wjeFSzfaIKL71h2iMHOkLdFk1x4uz/EmbeEzpGhpWrQttq6RFqgqQslqyok3YBj8R2gBqCzKAaa9BINLQ301FvjIvkO/3HwsR5YUO1g1Qder+EmBujGZtKURHN4nApMNQ5pP3PD7Z7bRyfi1CSLOTquc36/WcCeA1w/SGmmejmLKVcFWKcIwHvAoYMzTpKOZbHnTSujeV0D49M9C6uu6TP/+m9eJF437i0Hn+k88Bbfv/q5TeyEnr3DPDmFTr7PXftvXfsc9gZrwH71GbyM+ta/dOpMiUOiGxucJV4oCJ5WEqjv9OSUpoVJ6SYJN7yPUNPmgqvpvFeZNsb06vSA9yopf491DIhQZZsex5MkdEpZ9LLcwdmC6ZBLTlY43nhyjRMUJ1f5VRTIOIhXznazE82ZuNujNwXKeAr16fwLDOcfU4bcqQ9cmGAXOnczhWIX42OwDLVNp77QM0AH2siyOKJ86xB55BW6U6C0psO7iR3J6mYLr14MJvGpMFxsnhVEycVebJ9nSEC+KqIszKqyGEtuDMbxcn1NqJl9StfkWqUsusy54QUkKd1cFIUoPWVX1ofxcy5du65idIifIVJwXnEorWj9x4Ip80J2fTCzX7AWUBw1wFJ2RYaFPYbrC/EY/WU6grDlnSncMxe1fe4d4CGhBVTw5wngj3fLVb2VAiR7SO+JMVJ7sU4IQjsDW9IYps0QAEinrTNshRy4uD4/OeTmBJqiQaSFTAaeIyj1trVxScSx7Rve9b3m0SOUBq7V7Y9uUopWvm6au3wkixK7m/vdtXTSPzCslvxSNqF3rcqX/tAJQOHXcq/9a1J9wsOyp6rdZy+95dShzs9VkyEo8Okp8OUkWFpdz4qr4UZzqs17KEHH3mo88RXtsITQJKr8HMQxDoIy37uc4lZKEsGlGfcONYivGIdLJrHxodFp4XvjA/z2I9dCzb9PhwxJ+SD/jyJ5EadsXAtNCNCEcPNKONSy3ITW6jBOOBvlvjRICGR5jgJi7PSGQhdjsWcsZAAd2AdDIyddaAM25IhYKNiz3PVpz+dT1AuHJCnz6+MPfhf5EPaDCVZxNnGwL5GSG1MaDJ/kmn4oxmeC8/RKac0cvV6lam//3G9qt6+L27ipO16/Al0CRtrK++W+8LOfwqHHRJuJNy74z26JoQFt4w9fv+dC1tTajCSLGMYt7QtdcFEhaoMXdU4sYjAXjIrsgQwHrjcJmrAov7SsMH6Le7luBMv/JnP9KOVG6xQMXoiMS301WS1LifuBS1ZCjsQ6O3aUtbVurz7fB+tPySsnRzmPRsmrw6JKsbziJb4AbPkdjRf/QAvLeH0pGJjXC09RI1JUUh10U+2j+UiJLL1r88xlcNkwglorHNkAc02RuExiZnJltTC5EvJ+l4xFgIjG6GS2tggH3qga9lL3/Cd+w85t3p2tRscdF/eByrpHfglEQCrTy0jiqsU86Ed6e/0sfpT2G/PcOhh4UYZzJUBXVpCddiIyvDu3YHONO7+625Y/o+x4GGxcjBVIhlKfCgaUbJXii4ydkWSacLSiFd3fphgUTQsrPxjOPrp5D3HfcievgZzfnpUmWTYVLgIwetak5npq6OjT6C9+3kYLTDIKNMSNoqc101Puj6ayfjM9eanaIiCtJxxC78ys0UE7/3KBi4nYUxd5jTbSwE+HUHwUBRHffrZ/UVAIa9ygzjJhNRmU0tjNMPUFVNl8kiDmUOBxRs1z2yZHGjipC1zXDZdqzy6JEUUOsW7PhN2/E044ZDwh13CY9aXw8PKpyo7XdXx7qMmtBYL1zpDxAiDEA4J1rgRaeim2beIexNquoOa8CL4NIk3p47OVgM56BOFl2rXn4hXAs7qPIpLDVathWyD+aLqNACks8SsJYVzFypmDYpieazwqb8Wz9WQ+yrdzINhRntYuyasTXGAyqlJNo8BEYXhKqlrov3PgOqXN0Iw8sEH8uhsJKLPjHUOJLmju0sAwXjQIBliIWKW3alJCaaZ9pdrwhM//7tRty5dNe681e/IWZLSml1LBfrUwklmi0byXXIDpZhYSNnTYfLvw+H7hjsndOMk+TlXhlFrD93/RUf2NZ6kdxrggenBsAeh8gmKdqL1xfB5yuoTeyE+cGnlCCb5dDj3feH8g8PN8NwPw+tvDPtkLcrKYxpvV0QhucuLX7HFvWBcfl6cEfeTSxsdLyEQYC8PnffVxC2rpig28trzXhl6wC4iYEjIsTpZRuPQmwkIMI1xS0YKcDcWpIQL1eO1dzmcZKmgmI5YERW3JuR64Y6sRA2SGaJyd9dx5C/AxKCuecXdfA3W2Sy2eTnQxEmbl/+b+e6eYbYZQRK26JMaRSLlfxSOnhv+46Tw8ynh2TvC/jdOPfmol+3Kz9U4uYt9N3R/zC0JdriXt8d7qwkRb7+J4E2kgNAW27kVU0DsiGQ5UqcQiHUoCqM6BRr/iWnqpJP6FCdhiewohfv8MJAvpLCooAh9CtdV5bneISwaG5YuDqPmhDFdLcPj7dw6EoS0nn00iIvDaFgrhwAscwdK1LDGRJDkWvlIVUJD05qIltpIZIawF2bJDOE28oq6lFHZX+scu5CXijmBB00wkNlVgeLXrJk4dsnftF06e8m0n645anliPSUby9E3rVlKApJsXIAjJS3MUPJqPGnKp4a/OjNcODHMX1kZ/eCBr9r51UduEPNnvJFBYVIVu9M4eQxBB4gqhzLHj28557/2fsVLz7/64qev+fmaq/6006KVYuD6EJ7bCAaLNE5aCJSYMOnkyV1rnkMYcZeDaWZcmGD5yyycDBkUlWW741pWQ6BEeS20+dJfX6ETf3PECMdYa12U636uWPwK9Pg0SOxnaoarIqzXBgiJzdVJD0jEZIbeB0OatFVwoImTtoph2oiNZLOxX8kCSOhG6brK2+N/E17soxHUlRCNs88ejOYmKFPDDHhEw/mkRPCxjdvMYqUYiXAR7MJQL7sS8bqxiTgW3lQkYg5b1kelEY7sLuopUNe08Ozrwk+ODz+ZGOYuCmOvCSd8revNNtbnCue+Fupp5ESLF/jmyg2iU1pijZ4jI4ugpXTscgXWfa22hNUjwwqLf6HBORPFujLl/0seIciDpSRVLfAZHT84zhgOfhBGPg44Ro4Rq/ec3jJ25Nq1e7fd89Wx7//VqmN/sfS4G4e86P4wfdXa1gZvwUCleUxNosWtPfrGu4BKQ38aXsZ+8ZYj7n5mzyNe8r5Dn3fw8PKu9j1L5fvoddrxvr+v1zfoVpBfmrvLOHoEBKjZJdfa2n76P3as3Slc9WFu4/xdgBjII457/re63z1l9XdswBY2ZoouJxw0DHIiNEAisMNAYIK/8VPrPsrzWTM1aZ6PSLIYkJcrzwLKhQqQ9UsqYe0WOdAgQZCEJLNlnDkkmxFkHdf3+KpHFQq2c0baglq2yQbv1Sy2aTiwBeGkVatW3XDDDf7Gnk+dOnXmzJnDulcf8+bNu+eee1auXDlu3Lg99tjD303Dne3hLh5UIRqWXHbSCou2r6R0geXZZjkXI2IJHhdnzB4QgL++rr8Qp7ps2y6Gx8piQgNJk7ixSfRALSKsYzhOrQL1z1MqFCcWEZdZGhaWvS787LSQGAFWhGFjw6I3h4utOT8dPtpvzoVsPQM57mMgEZQ9UKIA+EFA2GgVoIFAWJYMxoD+auvcOTx9VPIauPsE8NwX9vtNeMFzSXRw0iTx43x5wFAtYo3LZTWE7BtRcrUqdN5wiJsRGz5tyl+fU73/tSu+Oy3MGV1ZeHLleyeP+f51p1xwybJTrvxZa3FOFutkdLQfKhpZhUmBGoLZs0TFrwyjfxBe/eUfvjo6lbK/lh7jLSMKf41HwyOG4Z5Bs8hHPFNMED8IpJu9F57bjmoDaXzTsImKG1m7HTOwp88CJkc2UjFCD4K0nFmllKAKs4g4csAm5wXYuq+FRBNWRIpPED74RBRSrMpUIcFSiWRJpqQuvO99fcriZ1qmzw+FL8YLi9TTIOkjUEWWMmgBSe5iSvBR4q3YuDiOhK1wKMznpS0uPBq8UbPYJuNAw4O/8VvkvXTvete79tprr93g/OA5PLijowNOWr169YUXXnjbbbdNmjRp6dKlxx9//MknnzxmQDbfjd/4rfoOHuxZsxI9xxkhHd+VV/ZIKJE6UKvQV9EhdCFN4IGPEspDLshA9ACcJFxU9o6OjkblTimvxGbaSV6UfQSNbUpkq/sCK/ZnbaSR181atJ6w3BKZvvEhOrO0S5hzePiN6Pj5wStwK8vCKMGz+4e7bOKRCCZbMh5HzqfnfUVFjnUX8FvRftV9Qfy5kughW/oHShQk6S+ALIYM098mCVkPspTC67T+KWHuu8NXjw7XUHA+J4Yfzgyv+Xx418jJo+RtAsHBkTo4STeB8izQpF3o9VrOl/S+/R7QzbOfGHJO5aML2if9Y/u/taxamWCQ44+fdfYRM9vbps9IXD/9EsOMxQZLBjIuQqbYadKo6uzlNcYrWyQ5FinPIMENLX7II5BepdeeVujB8Nn6Rx/XmZx2p4NrLqekPb/AelElq80+rFpbsVjIBAxpA2ScEr8Sh139jANp4dwB0c5kZQoZzSLpZuwp8A0bkS1AhjPGPeVAnPbFa+MZxYgsF0KQsbPwjTNkS0r6gjNxn0TumYpl0ls4UAmPmy6Xkoaxa8KyZKBBicDL3QU1Yrt55O5qcCyEQEndj3jL3GA2wwfY2hA0aQvnwBaEk3Bq4sSJb3/72yEhx0OGDBnabZS88cYbv/vd737xi1+cMWPGz7vp+c9//oExpGUL5+5W1TxCAVPtUpaHw5NP6ANPnnDreNDE/t7obtcnIuzcc5P0MH5FzgvA5DKrv7+mPjPY1QXxlJJtvRSP5tFG0pBIxEckbXBSfyqLs5U7v/5ePyCJ4rQFibmOiOzo6A5Enb18YnhubWjjy4p3lKTqmvCyWmExJGxWZ5C/5DJ0UtrsWpW4EXAjgEYkTTGfULbjpcc0rjhctgRrfe2xzuZBwB97s+tS54vDTbPCL+yalH8Lp8XgnhS+f3140aLpL6Y4GfPso9YjOqaUzDERHlnziZboAgfNBqGV1eE/GvfGU96xw8w9ViawQg+nTp3SUuFitnhg6axPGhb1XyxmXcFZLNVTkWpp3GxJ2hc0kTawmGEBf6h5GxV9hGcxqkUzCbTnJ1zCQ6QGqwvBy4KUlfdg2tYOyQG1dXBVtg3x2ARjG9M7ib8BC2TEzWQQbXCEMx5h4kWartJ5az6zuNi7B1KYV9Zj0aKW3q70qvRXB+scEokhKoosmCxlO2AtJskaj/sPx0prizyM/KSCcKyUaQxXgiyBexISauREI6aISg2ww87QYHusyiNvr6XFXjpJsFQBXVO4iZOyw7dlHm9ZOGnBggWXXnrpTTfdtPfee59wwglcb7j2ox/9aJ999jnyyCNbWlqOOuqoX/7yl7Nnz95///1bI3pPltTVNWvWePmmwrIBx4Mtk91bfqswNZvpjqApRu3Y/u1DxMAQiGiTVM2+M4blbmQ74F6qgTfHIqwOuZ0IKuqBxLfCWzf4da4Y2E9nnBHkUymS15WvPxGFXn5u7UgymqckNQa2hxHPhR13Dw92I7SElf7dFA7u7N2a3ntnrKYLUSrZHdQGSb0X5o4IdGl1mHAGhzU1g4/Mc0nKO2bRiaIf9yTIqUU6uE+4fWhYPTd5o6qs4pUlYfT0sPCA8KfbRr+YLUp7AHRvQhXaX0pwCwcQDZoS1WWbJMtWHUNgWriRgyfDxD/v89czT+tTFmQCEZhVoJbI/z4/r/tiUZFVpbQjDCe8hgbNkv1ojZBt8PwyIvPSsS69igXu/POT/eosE9Sw5gG+rMIApQxP2TfMiIzxMevEkHmHGk7GJ7e02txJRlyo2ujEEeeEskuu1tMHW/iABR5VTXLA1ghJmyfuiC3+ulyGawuG0ucduGHmZF9EuibfAbfjIAg3LOdAEwemaMwS6XkBa+SWZK6LmBvHIP4cdWuS5Jx1oGOPDGtcjqzrtA3/o1tWd0xOQgwYZZxjOoocdkcft4NWXQL2aRJspFU+5FhEcrnKm1+3NA5sQTipvb391FNP3X333cUhXX311QKSPvjBD+644473338/nAQk4d2ECRNGjx49d+5cwCjFSXPmzOGY+y2RmSykOu/wgDZpY3KAXSSV4PHAX9JHtMfgcBKRwWjkb79kacgPwu7V+PaTfuuMBT71qWSNmxXK+gIkCbfcIERYW4v7ICYZIc+rwg5/CEfvEe6bGObZYGWLjKzHi2x5W2deSu/L3UnQ281kTW+RCqnEeNh0FNKSdQ7oNhn/qEk6bz2XsPrCMJAlusTqv3Ycd2VemAwFQkjpVY70lOb2ZMuwxfoiSQHNxPOb65eWM4RYr2e1u5O0F8cTlJArn95iQAc6lcVh6bXu4hUc9B/bVSmMY0LgBete06UXJeZYk8eQxWBzNXtqclvTe0tnjvSFBUuYeaqtMz/mD8W7SPupvOcCc1zLyARefPnL1W6zSi+3XSm0nJEJUPCc5vZ15uvt+x3K6deABA3YNSi3FogAEwAEcBXMBI6YsUCkB5ahGkiChAwlTCx7ZI5wUjyiYu7oWo1kp9RHXYu9G9BAaxJowpLkJd8x+tDTrZFMSmZvhHpWd27kiYC2LcCQTBPORHI7H1i5aFHWO/MhG7sGFFouslT5sHxD/25nFHz8RLi5ozz1ppm+uAWzrr8WTp4aPPEExSb13Lv535bEgU2Nkz75yU9+k0YtEGAEIZ1zzjltbW1dXV1cbJ/5zGeefPLJHXbYQWQ3H1y8AloCj4AkNqS0DuDpne9852lstd32pLdkt1GlhZoHG44DaVBIZhAScQbr1I/uJPrJBctlRnXSJyXDy9YSRWF6staBRVv099UqMLjzwAcVQrFJ36JfPIneCMGWtm7qDa7W8qtigkdJdL4XXsn2/4rwg0lhzuIw9rLwxsfD9CQNYV+CEoSJAAQupLapWxYOIRf8QWxgdbiBpUSwv8wMdjXCl752rzj63mC9vzF+MFDVwkkci7eEI84IX+t+026ym3xcWLgyDPtDOJJORWCBiXHuucm2cO2kq1LQrPH88HQ/vZIj2rfB1H+5C0u/0mfW/UXSAKAHUIBFLMHi2KXFAKC4GSpnGPBVLB3lB/yZTngOKDSyigCFDWv9TWHp3R3Qyh4lwFfDXPXnmztv+fmiJ5cIlk63N/aiJdCB5UP4sPlQ5Ge22gaPBfpYn0JgjEMxjAkmMJS674FCLGqgsNAo0IQ3yk46T5aBK52EZjLzVYTgCoCebFFyYTv20fiBEhchtWBmxs4aYrILHEkRiXZ6IkTdQbHkEjtcHKz0RqBMhDs5DE3WldrDVOIliZ5NBxpM4sVmG32mOFfhj6ogQoYl+Wx57rTK8HHNWz5FpqV3bx5sIRzY1DjpzDPPfL0HpUA777wzDDTKOqKb9ttvP3ajxx577IADDpg2bdr8dZp5+fLlQrnHjx8fQ5diYdBqujVLN/G7jdwgAiBW1/xbxgGvIpFvsEizZvVZYOUKUAAM+NZzpCG5KfKD34S8QKQVwU2cWYb2S7WEbK0LiSqLP5IxGv9rFXMeVuMRoK0jEc105Makyv9n716gNbuqOtGvU1WpRx6Vh5GEkMhBAYnQ2A8Ehna3hS+4ku6Gq+igBc2FcZvGwcNLD2n0CpZA+xgtoC14R/OwAyO04ECHIUMhetswBDESXmKCNGAeDeT9qKRIqlKpU1//9plVq1btvb/97e9xTtVJ7Tm+cc7+9l57PeZaa87/mnOu9d2ZHvX/pf/r8vTj35Luuzuds7f6sbaWM1WsXB0rRYIj2khkmF3N3DqcQbQU4dtK6m+q2b5kXc6XamW/ds1RjU77x9J1afnK9Pz/M33w3HQXQxLV8N70EucuRc01jf4W1gqO2KtPx9jxBCppAqhqlyULRJMEtOXd182n096hrmD9cWT8OMuRyYEbznGaKiYYha3CXPBptc9Jww7RNEWMKyLu076Tgr2OyYAhhDmE9rWpYvPoocelmzbff+Ch9IQiQM24OTqOKX4Dhs+I+3VOcr4RYMFGIk+lYwKXH+ALGcCUJp1u1YnQYQBo6EFtveUR9NMkrQCR2VyDn4SDVYFx674ipiWjnXELDMoKQbbAioHqE1DJxZbVRRrhQ0CRP97yKGZK1F9zCKgaNe/kBPK0bAD4WLPi1DQjAR9CuOEGXKs4Os1RF5pG7Bh7wLG/hI8KD3SicWC9cdL5q9TKBfvaeM3CnvTVr3517969jEnA065du97+9rfv2bMHAOKDe+CBBx7zmMdkp1trVsPNNeWAbTikQFNyEX/ko3VSk2Ajlnbig5ggNaw4Gf+pEH4TchAtL1erKxrRowAEbpJW+TrydAdo6Ha6kVMW1gwtsEUoKiJJbcnf5z/34b1/ce3f//+3HNq0+en/9tue+4on7rzg9M1blkjkkJvqtkAiYRVtoQ/cUCdKERUrYsOK+YiDjwLbvDed5dNRLgFaEp7gpB+C4CjEQLqKtC3JglUMCidRuce7TNDrGstIemUrjzIBM4NHbS9rqYe63gBopQfTzt9Ir/pU+p6npWu5IP46fd8n0vf4Td8yMY+bKC5GLzHd3BaimC2vgTzIuEkqJbKkVd02E/e5Yxy2Dt38rrFHvQlJ8Vk7ooBj8dAc/K2F4gMrl/5xcXbac0662w+dOUe7itY8SsdAJbfNDguVmo3kaPIeV7Q+NxOoShSwwzEs+Rt4yGBA6h9NMDh9TARzH1wAbWFBtW0lxifzF0lg4hjheh+CydJGnkYiUTORWAGFsVtBc+kqGgEoZg2zlgqY6cYqxxkTuE0q0LlRJ6xKQVFzF1GomhtpppL7BklMApZCUgigbyUvmuAa4kJDtFomSnRtPmGdF92UAGdcyBbmNqlZB2FxUEnXjJ9trWUON9eQA+uNkzqaYl/bNddc48wkFqOPfOQjQrkf97jHwUkveMEL7Hd7/etf/8xnPvPqq68Wq8RD15HP8GitOUAAUcOtYWBkDSjQJMJRFIXJH6iI2LLuJKxZvOke5A4gxc7kpnUnCeIOSWSJH3ubpQEvhInQo7RCB5GDkq0K4kP33nNwS/U7vs6i3gqBXfs/vrmULt6RHnda2vuld9/3vnd/8eYtT3rMU87ka+PcWaxgAg1J+Te9qVpDh8AlZ0FMjhgAzmnj47xUHU0rH2mgiFHOOO4JBYlatXhVEIEuDklYVV5Gl2/1vdYBshZCArxAS5guvJbRL/qvkQsfkzP06BtLZHK/RhquNw8cOP2q0Q9flX649jR/pbcCEeoIgMmngyhj6lM1F0IQEqFCiS6K9L7f6Ijm6AiGlrCRTMxf9+k7R0gEG1eH8cSXqsRLaeWM9KDQl3ur32VrsUqWuTC00MrCqqYlA8zMNdjUkE/NcHBtWaKlHoEC+jpIzcvKeyrNxHUI2QIDyVaL2BRVkkkJFFNWeEXlX2JxXwMjSg9iZogGXfHJKhGSJmTkACQZV4x/7ILMaQaPypBIoJI9g3zuahivl4NKGsf0c8Eb23ow5A+RZcB4i/m5TFwy06TxqHwa3PDXTTVRc9dclpojZzUxfxn5DBXeRlKUjO0WdGVxw/WacqBT4axpyY3MHQpw3333QUhMSna3XXLJJbHfjTPubW972+WXX/6JT3wCSHJ4UtxvZDDcWCcOkEdiwMiOJr3rXe04yRZlYoiyDCIjfOjUEExxk+ywluKnJygVQRIRUqSwEwdAHOmJDyokb/Ftlu6Ode0RkLSyM+05L931cNr2QNp6V3VG0db7007SaX86ZUt62LnfZ6S92w/e97efP/1f/+vNfkuVAF0gWRk7HrMMYiAWCWWyVbAtgegTonPmQqkBpQhThWFIW0gUP+mVbntbr+KoEdsXLW/xHVFZ1IXoLb7VNqJxhRBZvos886quBA6oLstl6k2/++pmqeGa2fidOBHrPcnYMEJol+48e+aGabbsRSRvz1c6klGEIq5Ef4erJVyHQon7IFfGEs5H4xxANRGwX0tLdTu+3CWOSk+/mc5wrOX4ZNUTk5GFY1pSDeBPLxt1FjDWM7C+IZ2HcXc9JZMgItLGFW0ACzYPd5vRZ8zoYkywRlJQyJDIJ+dgmLkjpaA9axLsgmlYInEP/xVnGhq2ApU4KE0ZdcZkGUIn3uXtdUcpbiq9bI48oStpLPNAFh8LB9EC9nww+cAxTNRMTfKskfoAyv6quTojF75mynJP5uopB/WUvzWPF+1MJDdY3EE9Lw503DlwAuEkMUm/al3fRhdffLGw7rYnw73jwAHzGaRoxUnUZCvRfyHuvYtCZBAK8bV8xR0iLBPhRSr1pyN78UZcQI9PN/lRsxvS42xHJ6kiE6tcwTG3pkftTHv9Hslp6QE/7n7w4A4/JAe3MWgtiuz8bzXL0+ukLZNPTXTOVq6DhrneENCwKDVfdY9wIVmDq2FAOnRodNtt+97yjr/dtosOa8bcQAAsZHrZRjyin7NDIAiJr7Ef+1jlRKMGZLa8XF1gS6kzouGUHJMetdSTjBOdBRFCxnOSkcnsB53QiwshkbwWErmNDKKYw+gSplPMWT0h7mhRfDT0IpMJMw8uCcayAVBImYPg1Y2ijb0FR19oXK1q0033jnaemXY6+P1YpSx1XdniG8/RtATyml/GBeZT7ewfenOxxFmcNxUaVOAODmALxIMPWApp6aZADzGD3HTBHAj3GD9GHR7iJ+6B6TJRWyMQLnRWgiawk/JsAjpMPtIE/PJW5F82R7aKi1ksE1NMsssuq6yrTi6wHwm6wkaQEdYpyYsBj8qb5XUeG24qQrbuaKnZphW4isnEo1K4BZU70PHlwAmEk44vI4bSp+IASUTcM/OURDqM+4ERUSYUIU3gReKAKmVjoOAXHrRIJ63S6Jx032npwW+k8+8WtnGMnqAzlrj19qRzHIEtfDM2q5NNjGEk6aKIX7KUhmW2ouDJ6G5JWqbvuGaoG1dKx1sdj3TNf39/esp7P/nd+x5OO8+wdx8deHDTpoc23XPV515zV+WsocLtuauhCsDIUpvK1y5AKvCVdy3lmZroVEqFLZBKoHjoBkQjCgB3Xxq/5edU96nIEJpq/Eiv9CYxnMWBPc1Hs90RZlfrFMrY7z0HQZNQMh8m+2hoRNY6gT660oQyO/w4jByc4IAz5hREQp2z5Y0bMFS4+vM0feD9O28dnf+E9FURYLemxxwZ9nWQpBqXXlpZ/qYlBkv6G891LiBiqCyEtBHVOOYO5oAg2uWCASYSuIjBow5cVAYh15U7QDP0wzLEwBN7ErFLGmjDuktVBQgyg0kGcuE2QSRb6YMkNjZqHPZWtrpFoXrEKwKzOPIimClKkZXqlU3QqPJO+ehwkUf+KcVHekWYGpYW/pIPQtrZupghLeE0ZKDjyIEBJx1H5m/goim83burLdwUQJB57hi95qGU8dRK2lOHFMMxhAKd6iA+IsDiabHE0MVhQUZtXf31Tidcr/7we7MQRvCRXydlWFpJp8Rjv3K1QJykmeMImFhUADLRT4PSu/OTdbY4J2rmyg+nn7zrSb8+2vTwnpXR0hYi/mHHvaRDX3/4XFEpPnAe9dD8sQtKq+ny45vwod4ggIBWWWfACvZJUfO0jnV56Mv+DfEic2NPApIc3sPzYp9gSayVjD2LJZ7HDuKu4nLFPfiMn4hb2aTIPMFYsImRzMFLAmuQOWJYOlWIa9jI4YuhofV7EKaBraztVKndLf/9fWd/fXTRzvTA3WnfgWr7ZAtIsmJ52cvqMPdwdp3/1FwFqHC6HIBoBZ2dGbQ8VH81z+inloJBCMLWd4FgYoT4i13ugOMGAFuOj1oxHamYMQZwSCCZDzyE3FSE+htvYp60IkiCGJOR/+G7bf80li1KVeUsGIAXOqx9RrWio/uiYt6Or23ZtNzzlkxUgMSA4VQ4agUgKtGBUmaHlsY0KW3tLXkNt9aGAwNOWhu+PtJzJbsBHVu3/Og6573oSMtf6+MOsko24QkXay84ybU5T0AsltTK9pb9+5fuT2fxqT107I6qsiwKZDW+2xnPh3VJf6Vb5jPu2nqdRB5HU0nScZm4Lx871ZkHQh90pOx4REbbliy4FQaiaTDkD9Nz/1n6zI8dutxpLytp0/b0kCDdD6YXRSbsgkIoLrlkCphbVo8aCIJafGYm4NuQc55WH6Jp4HLhsfxrToDUBEOFHYuTy6PFUqi9jjwpcr+iKNYH2uDtbQ4GGl0CxxWGq85kUXOwkrKHBkAHGyaE7+g4hj3Ha7FRyUTo2/nnn/L777/wzrv2X/iorf/3y5fYotws81c35pDItqOGzUcygaQDAoIvgUWayaa9Q/3LyqeVDBVeJ3VeHZaHG5Kbo3Xhk5LABVRh2cbk4yv0A80AN2xLbhI1IA7JE4dx57JkFbacnKdHCi2/RuJIGYsfgBu0xXwFSexmpM8DO+ff58K7ais3F/pXbrJVYeLIB5J2sq4LjHJ6uF994YlzPdC6csCBjY8kcsDSM57xjBtvvPGR1KgTti1XXDE688zRpk2jU08dLS+Pdu2aXNOVldHBgyPhLmtHN900Ovvs0Zb04Hel6y9MN1d73SrvUfkhlR8m31ZvVoYln6Wl0ac+tchK/d7vVXlG5mv6d/Pm0XXXzVXza68d/cAP1Gq78rT0uU+m730gbX8wbb8nnfm76VVnV0cfHW7R1q2j//gfR3fcMVe5c75sIL3znb04fNppo1e8YnTvvVWBDqm95x7RVqMHHlircXjJJb1qlZnZemEMf/KT7RwyfVT+zjurz4MPHk3j/t69o1tvHX3jG4cb+M1vjq68cnTBBYfr84QnjD772RlbTaY+/emjnTtH27aNDLlFje1TTqlya+VA3FQQCeNvLtGFt7ZvH1144eiccw7ff9azRk996khHq+G3fVsljrZsqR5JGTef97zRZZdVZcnNI387Cu14JM8XvnD0vd87+o7vqHKWVUfiqR5FA/1VSfPr3HNHj350dXHRRaMf+ZHRD/5gxf+XvGT0la8c7fHhamYO3H///X4CxPFDfXIYcOm6otJHUmEf//jRDWJWeyIJfOxWE2ZRI3ZpP5dtScTBEVbuWoLFfrX4Fjxx+y1b//y/HPja71x+xUPP/nT6p0eKCFOGv5WYLMl2J1axBZKVn4N2Wk9PWGApkZWFck+yBGcCjNOHmQOd/Mm5o5J6M9bER/LZ9On03T+ePvDc9NGz0j1Xpx/5bHqyDYtHnlYrdQ4jhg0uIduLjgsZS+wiPH0RaVurQ16jC1thRWB0ibFnLb7WzgscZk0UN7NGFFaWZniv+2GEKMvV8DLUfeYJKDKJhUYUv0AfFppsQTl22JQl97pmQekgLdJf2WDD0IK0UaeLiy/jI1VP0LeUrIMOFxD6HeFokQNjEjuTtqutTOLTYcdqrZKs5CMHWxN4PxUkB9WQG2vQnCRznyC5+RCb+GyiaYgPkoBJj8tPS9VkoHXjwICT1o3VJ2JB5jzFaSoinhGzsY8YJS6dUSQ4t0m2O5Uk5lHojLiKEKZ+MOGlL63QQ03EU/OqIY3SuRVKH02ZW/9r+Vxw0eaf+c//+M5XPemc/3bf6A/2fePuHQcPLpG5tbgKomd5udqAxi0Y9PBDKzd+7t4P/6fPf/VPb9568fIvXfHPzrxg57YdUzsItZHrRCQK4EiYduuD/k1rpmSNFwA0kfhKHHHklxyydmHSJ/GFqoQqauSwdEu66F1pbOiT/vKrC3ZHO5l6qnjqRkHVDYpBDQ1FlcG60IitKcubfkD3ta+tQqQpyBrpWSQ3sTuOYuozsGs5zPzVPOI4sy3AULd4mI0E81l19Kc8l02fCACPd/FhIW03gHUKjAJl0t9BCpW/v3OSTAJtNLMyMPJN13jrqwqQGDUysEVxBYYzzWPGQcm2whmfvHLCmIgX4ktxwZZx/r5azr7CJTJRSa9bFuoas9uFCARjL4coNV/sf0e7cktVT7baawCTouKWTA09q1EhTAac1J+x86cccNL8PNzAOZAsQnf9ToI5KQTb0tN6kSTqICGQIrKdAJSn9LjE8hQbkUGSZKa9oGOLIRGsBEGQAILf/M3KyEHwUbpOAxL4QrothL71ou0/+wafSpCpubgophTaXU18SD3ttQc7n1u9/8GVT7zr8zf/3Lv+RfrcD6X9B67fesXj/8npv/raF/7C46etjwZCMH7jglxzvCSL2sKhkiLoAChhYoQNfUCsO8qB/iiJJUakiz1BsxGu2nEt7mcenBRqD2hTw6uuqn4WjZUrfqhEH9FDHSqB5rDFkgoxIClIHFaT+Lkx2bI6xG/DyWQqko/RqFxaKg/UnjlgtbYoXTgR+vmfn6XfxRvpWaX3JB1hiWIHg/2DzKKObMXADr71zLZMRj7IkGlKiBKe5OmfL8rEU13LzUcwFp6TSBlzKI4sii33UaIJq2uU2IpvPLXvj8VF8JysfJWDCxFgzD82x+GqX/Kxj8yA8dTo6k/yUQcZRqSUQ/CD+vdRd1nBxmimlNFGAFclITw8UWHkrwpMVfPucoenfTjQeyL2yWxIs6E4ADcIARYGS9mYnxa+IIstbNwxMSdbW+MAPTqpG0vFi0JlQ2GU+QBPwlcVHSflMI+/+MXVj74F+e0nb5ECxJk18QKJpgxlSa36kLNkpVbUltq3XH/X9f/PB78vXetEyrvSOaemfU9P1/zV//s7f/+cX7v4n8xyjAneslcxVmGabUr4jOZXLXCkCGgblyiGPr9CT73BQzWQFOylX61WZyNKgutNn85Dlst+9B5aDbbgkl/DZQTSNXLWRsjJIRS1nsolMj454pxL17iiQnhdGcnY8IwiqpfVcyIKFG5PFcElHIgUkk3jXBs45kV8phrt7sTt5qSgwg1Xg1lZOpqvjR/QMYx6ZNouNh3YxoIMS05bx6X2IS7m2MPlCKiwXdm2aeERJyL2yaFnGjjJ6R5+XAjpd9NnURS8stFS3wmR5tfDUvmDJoGKoiBzJ6aPkdCKk9w0yJ25ytodUztkCDsl+GWmOHgCWoKeCT2Sx54SY68/ycenRoaorveZhyHaFUzIF0pxjc/hUjcIFaGB2u7ocPyxHjDapdHG5sisVXL4OicHBpw0JwNP6NeJb/syoB8TiW3DErNUNo4SFJXiQJEsTagov0RG3Nf8YrmRhAL7ARnE9m6ixlIvP61dUD+tG77Uh0oLnMS0Q2aFRPC6CrNwWPD5BQnVWDvCh1Ybw85De540+sL+tMPv1Cn9gXSaZec/HX3qna/7ytuu+u6Z68NOxlBPASB9gY3T6lEv4lK8TtzDkU6vtkrmR4j71bMxRLzqCAC3laii7EZpTdBxk/i2n38eRAuX2GcHJJWEOYxklIRuotic9eB0pW7fIkDjt4GDHOPk0014wvAAx1M2fjEQ3HGaF6Sia5gAKdoc8qW/HODp+ACb41xnAjr9IDH7aCwGDCf1tBdJwNbEztVlZRoTij/UmUlTEYTqgAZH7MCF6p8rLBPqnIPbOIEvF0W6Q55KZB/VayWCmb8I3NARSM6wS2Tupm4KyuzSBW7mr4cfH/nHKRY72gLJwS7Skyou4GDX7McApa63NnDgNUA8P6nPxDnYXUq8rlE+OSsXxn9+JAdmY2ITjneQFccuZCkBIWA9wFI+0NpxYMBJa8fb45yz1RjAYWlubU1qsMZzZ/CsZReJnw0nj7KScwH3ADFeHIeTSASCTG4UJODVhEHZHK3xrNx0W/MsGct04CyIy0+e9ERQCEHZTrXOO/zyIv7ZpLwjPWjZljPzY+FnpG8+eNuRiIz8YJoLyt4BzeQag5n2IjykkvsTcRlcwlK/YNoaHDYuN8xU7jjVEpUxEnoSmOdsKkc2Sv/4s9OP/vAZZ555+ACqnjmUyZg9du8ubxy9xh8fkNo+9vCbAIVGyPwkbt05CMYeL1WoZ3ledlnl/BUCTA+VvKJlrSjgJEjIQcxGvjpIAOJzH+fXXQj/8mkl3e0VH1YBug2ZR65NOvAO6p32BCy1YoRjdWuOIkNFQaASl7czMhZImBOWUc1ZC6jEHkbyyFn9Ax/UKq/VivZXglYy1FnXTDHJdFPYeFz4CPS2XGGb1INsYz5+WKaVgoGtj1pvRm2nfavMSouCyqxMybgfrNAEFBLYmgfOM+Q8cswSacD3zeo50BpxYMBJa8TYqbM1pfnR49g983mGA05qRZpLZdCuw+uIITLCT0cFkXqmmakYMzCkjyVLfK3lFl+panhLzuxJpqV1jDzzQhYs8yNKmbgq6AAmq5KUCGBlV0iAtiwWQ/yRlaFLyhfX55oZ6RvpouX0Dw5kwRguMoe93br0mGe88DvmrIDDAN/2tup4QLAV97h4hHhHSGm0Wv75oiwrtKnhgfmMNy9/eRWsOhWBv/lYwuaLuC1ijJspq/xmmnzn1LT336Y/2pN27klnLKev/dB91++6/bu3H3xe9Vt505ORQ9O3trrMjH+EyYfhBNxkExX3M8/qmZ+R+UpYXrNcjBpnIgWVuKTxnxfV1kiIzQGAfTgWDYGxuLMVHbDGFHv2s6tzljXETOdbzMuVsuEd146AYodrgiSv5HaRJ+PIi1A7Mg7DsCqmh8NOi0Rqw22tP5EbNrOwOgfy6w+vx9WkvK90i7Sg3IoygZtKJMRCWNXS4CEjH95qXQYZXnefXc2yUPwWe1K0t8y2di0HOQdGqT1q/TpV4tYcypty8yEnzXcfDDH3oV6tNjgBQROW682wiahEMoS3kfVdKF6H6C6LGK6n5cCAk6bl2JqkFx7hgGZyitfJDGGJ4TigWV3PRmYXBxmvfM7B3CMZLaqomfB57dpVraH56TnsJVMH0h8MIijHkWTCiSzHZWVOQjNEvKUMzW1KW6uVRDxRLcSTA/GCvA6l2WCV57PTKVkLyEdowNOwtUiTgVSZ4TpcP3T6uTc98dmP/vLvnZduF3iwLR3Yl7a/f/Sig58+/yXzFU+vcMpQlhZ/Gkvw6W6cwTdsJJQxQR/VNB8e8kAJVeHskJKehhWmDXKXCe3eSniuVnpQZXwm0v+R/uxn0ntuSY/enh5eTjdduOfrZ73n/KXnfE9lOZTXlMR9YPj1Ib4tvhKqAsi2eU1AkkE7A+Ez9NAKkrpz0y9xFrleMP6FQ0GW/YlKE9DtXdNc1zsek2VXp7gzA5ndmiBIpZtkHho3J/OVZdEuPO+a7/gJN4B9YLRjyrlyIr01FfuWHRU1MlDN8Ve9qop/wgSzWCZhs6mlnPlrAKDu12Eg9TShdIqLIHVTH3eMdpQfESkBRs0vizrOSphJDnphHHklC6gyjQGei6vd97X1UZlsqmsiwhpSNQI4ytyFNiLAyEKLlI5KEtr85sQsfdEhuqcqfUhc48CAk2oMOQ5fiQYRBtzq5mesdSydTWMakVaYjSzBa8G5McmFyDBZBU6ylLQfzea1+IEkxcFAtGbMxnHlirXkXKNTKTkKG9gSekkNtGpJmoAnAlyzgjeH2bfIrCg98rds5bzwlORCMqEFHT1cphlXk7W4v+fAae/+xiXXpLN/KP35helrN6Rv/2h67tXpey/41CbtzREwMxetf6OL9TWfCMFNbYeEZd7zUxV+8ZTBKe7wTjpJgcMOJpAec+iGGYhl3iFJLBDB5JyDDClsak+Iq/xLJ1ROc+zF6Hnpj05Le09Lp5+d9pyTbt++ZWXpxhuq4Sv6Yxqdz73rx2LtbuuvXaQ0KegDVihWJQh7BqJsjP/+hTaLAAtgFPykq5o+5WZ6d/DZNBFPxgQrZkjpdLmP+7ORCKqambaZj7lpsotbEnGo6xlaDCdjD9rDgUy+WlCBOznGX/U4vnkVxQg2B7wcLLQ0nNdPd4T3TW7zsDRXpucFgYn8xcD4GL34CTeUFqlgrymjabFidMcAEGjPAEPo2eXKNtZKUUTtEeyCh7XxA2sqOsyQC2SCrML/aM6qtlYohYjQrWpeTmRPJXantc61JgxfZ+PATHJ3tqKGt8ZwwI4VqpH8zT4vKCEEXA0nUauCKmJVJ2Ij1hOtucaCo/mItZbWDJIPkWeBCAFYfrFVkAIdecZbEgBYQp2CzFKfceQRHe9jyzdqIjDzX5QAYCS40lKPrcVOHwupjjzHlbWQ+wcOLN3wwDk3pEs+nJ67Kn+0rfoQjrt3L+xH2YIVQj1YF+g8S3MdLVR5ebk6E4GJhaLCZF0Me0SPTOyXjuZEt/6fAABAAElEQVQT5SxS8BBPTZawDIFUnehykhe3OUzZbAQmUzkd9LH0/Q6gYWZzcMy+pdM3bX94idKiNnsLaTJd7DODECg/AymH2rN58NJLZxwk8yszOQAfKqAVfdrNXGcxEJMrz74Z2p5f0VOERjeBZWyQzgIVoWh26+LWhqs/zNokoJl7vYmTpOT3MU/ZMAzdGE5msWHThxXNgso7Brl6do/AnD6SSR8EVdRIe6PVUCD8RPiw03MvQo2+agVwYxZA3hNJA7Uu/upBGco8lnwkg6Lnb3utDvKnEaIJHmksnEQvKJrrjTkQaCMiVAny00zrlBx4Wstq+Do/BwacND8P23MwxBlIbebiojKj+LMYbAzrJplmZqxxX+pCEy+v8LxiztAuQiIspiWzsHjRiw5DimaG7lB+PCG0rBdDlKgPfcl+wCCUySOzi8F2Wiqr2ufdJkLKb8mKc13TTiSqsNERi/7heuHkYknDIaHaVi9FGCc+iyVKWkFsdRbTtCavrpFQ0recfeglz7lt07U3f+7vtt2fTrsvnfnNpZ370o7R6IgiWk19RXr+l9KTn5/+8KnpC1u3bNq25Af0tlb7wQzfHmSoc/qAF0bjzEQxoKxCuvMxj0Lf4LaaUjaCseYn2dqeBtEyAY7z4JhczEgay4bUjz1966UVMu/mISgAxAgZpGKVLr2vna+s2Oyvsw8mPyOiN5c0jd3Or+A1icJ2uAa5oXWM30YUWTeOD83X444qobJKrn0ISRn2pHhF4nFIRfOlIX/gG7vbQEwXLElK1wq2GW20UEEdLA2gqQgYxUBiVMNSmTBTyVl3gC/jKtCzIbVkKqPm/ipIxyk0EJKbhrFCqRXLE8JcGvPadHYx0BpxoE1vr1FRJ1m2whfYSAgR88cItpQRlyOOpykxOaQAKdLcbAwgZWLANPnwQ5yzUdnqzdPl5WramzM2s7hmkzCFWkkIhSW7LTyx65tQYDOwEByogwPjZI37G2I7CSBCeurx0DcAKCFOgTE/QNgWoE6XsZL21B0Q2VO0cnD0kd+/+1d/cfN1tz1VbIY756V7fmz5r7520dP/8vNnxvhZZdrSN9MOwUl/k/75P06f23Ho/iUCW/ioeH4Xk4iIZz/rs22+Iyc1NzUgj3HDvnwXN1gLuKj4oEFPqMWUGRfWwwSLXXG4l0z0uLJaySOeSlxlUmL3FR3cmlIau/kWFWlHOGAgCYDTjI4saqHdW2voJuus1ZTqSa/C3cRx9PR03TPSJ531cUN64sfT992QLrznns3GkohJn4w1DRgtio2HEAY7pTR6BDewGjhDHawrq2EtJx+vSx+633IRJ/uDpDK31mu5yRwHmLR1vSnA/KOj3UTErLKyPSkSu9/am8Yb0a2liEi38LAvUj5uhkWnerAgUlt1i1nsAs/1u1IMAEzTEJ4HfAPyfBUsYQpmZaEjsFENTfPopgVV6qTOZsBJa9L9xArzD28aAGSUm41cZpAN+F9zpSmeOLMgd9AR1UVemKXGujtMUJlstzHJw8xgcpoeVja2z4gNGhe7ZyZbu7OcC4AlKQg121iUNVAHBwggLGUFrBFmgrlTkX6kp3UW2dpHqU+VeTMxFcV/Kt5CoBvnrNJJScFnIsCMQ3BZJIrKiNu1DzGEL9zgqbH0xb9b+f0/OvULty05ys7ylXK5I51zze3f+crnfvlJT/+et7+9GpBB+9Npt6Zz/2d64g3pKY86fdvo2U8+9xdfuhl3ehDULkCYrJ+HTBCnHPU5WpNeESYvli6c2trOyeInVriTmoRXv/Eb1THWkJz03nXHdat7C9NMKL8rIrLHQqhVrUqguOlAEtb4aGFBZj11COepiS4mAWwFYDwwSllHWov2tnJp0wDEsU7T/HG0Oe3/yXTlT6X3rf7OMT/qn+xKV/9WevXn01PEdwvutkLLtm3iC9S2Wc+FmuIY5GG+uLaQAB1odNeqmutm8KsDynVwB38gDOs96V0YkJEgvxWJ89fq8ZSUc5AJ6CAQ0ExUk6iMmxyOWldSR3EeedHAQCxSOsJ4VnONZUubeVSrZBSaaytPcoPKiEeeYqyu5EXFNBZoMtwFTI/EkJWBeoZK7DZQTytzywmDUA0HmpMDA06ak4HtrxNqBKjpFEG7hj6xa+FiajVxkvlA8hrZtEhMBprMdpjyjDhyp9S1XvGhvbrXXgwGolIG6s8BvSOS2tYksDUTVjPO6aP+ZABALcAxiUZgQSfseWtHBo+oC5q+jDWhYm1ZArgp/jxOHH2eTz93hhCLPT/UX16dPnPNKYfSyqalFXui1XPr6OA39p26Z3QqjShK22A+ogmW9qed16XH/5ell1+x4+GLz378T62c9hShG2OMmmWTKXvxs7MRbWo26R0GDAvo0nc8LkPMt5VSY0O3ScYAw09kDdMky/E4wUiU2L/7d5XqMt0U1zxih0nYsR2wwji7VGROp2Zs0Szu6B0AwZwHeRhtzGdfTVpNPf30fUs7PvvgxX/zlbM/8VebAF+QwjhUT9BEDZ2KyY7F79MkyYQhykyMkYbEp5QetVeemr783PSnO9Le29N5zsLgq//O9HfPS1den779ve+tTlstibvH7LBssw3TGozaJmEwGULCZ2FbqhpDRTXIPeVihToYgeYCMQisABYSwy6aq2el1ykBX3JZXpmTcg4uZG464KGFkEJd4486I/VUySPD+zA6aRYtjXy8C3aocwhkTdAcH62L+nvaOsCaGcadXEnM8S62uJCbTFQs6hbRSEqJeqo52IQ8ZRvLxIJICPzJn1SZeAS8Oj6A4KptQ87ph4v+HBhwUn9eTZHSdDLxDOtMBm7czHfKCzYhCgk8Ii29ZS1IVJZkEcn1ZoaEo8RMMFdpuJgwZcrheh4O4CpgwQ4H5Qj5ZJ6xXGMUZIfoTwSWcCubucKexIrupAOHNa8dVOJeEX4EhTcpo6LmI1qW1dPIFGE/Gm3moFgZLflNCMfy8RXvH22+6przHLJDGZDdoe0ik0Np+/Xpu66/PX30nemW+6qDoKwBCHcBKyW4r5VokNsrYIRPS2pof9kLXlCZUuzHjLXHxExoQXrCTCmJgmylch7laQuOAIh2fmVlxorD082+0g2SFOGVyUceqBxYYUeoU67z9lStPffch7/t8Vel5//mLcvX3H7WyqoxJqrNLegoKSQ6ynGjIEsNXkQyuCsrfndUpvwaafLfp6YvnZf+1750+uqBYcnBqs7C+I50w3npzq+nx+Zk+UJHq69zGpk0iCl/fTBWEcbhH/9xdfSAMCAVCzhrzQCgkF0GAMujC4nJMYZMLPIWOGXeEY/quToajzI8FzrDhayi4/xVGR9l6WjTEOMNfiVKY2zrcRee+guGsqIxktVIMh8JfExw5vy4I5nK+wQ8ihJr7/b5qnTiAuURq1ZyC86oDy4hBjBQLxwLQFJ5lJqxakmjegSOv+rD4WDtLVB1nM+hynGgHhwYcFIPJk2fxHrOBgSBEaal4W6Kkg6kiRE8jswH4SM+rWTdz8JB55kASGI2VY45S5CBFssBapj0ofLxnBzH5FKJ9inLIVX298mHREOkqq+sSrYgZR3cJ5/+aRhOWkFSnxxWJbtR5f8mXh9hr6tvVePszz6z7c9W7WqexcCrZUi4294vVIJTg/rEKyjTeTzGf5OMbZ5fSnQGoo95CVkv+hMtaNHfk+yKaJJCHYPpR81YbuhFQ4I1i9surDgYssq65nvVHU9zyEh7CnfBGZBc7viYSaZ33vmlB77rvSvP/MxD56w+0COr0341Df1nONlDB8E7xyjjqzKDMj/3VcbH2NNHzWrr99Xcj8KxajQkDrhinZdzX70gzSAhkDcv5xiKEBS7vFz1MoMTVy+ETVIFPDI24ADLD6MFQsKcMJOoEmSg0cHMqB4IQmaiDg5XjyeR3IIU4dpHlVRDV0Js/KryxxZ/I6VyPW0KVRjF6PWUjTCWu6rN/6UhXgx84458VNudXO3yuqOy6hDtlV4p0U0yh4QwkABRBMZKQyL5qjmOhrd4s7TOJCLWKI0quanOTFAAFj4POClzabaLASfNxrcJb9GRvAOwP4dxzEMjXlxFH39Ba9YWEH7sXTyEtY4MySNb+mugynxgArGl3DwnlSj7LMVa8xxudnCAwLIO85mWiEj4mKLiPwlyQbRZ21mChzqZNs+J6a3jF0QUyxHdUilLOuRwxln0x/f8ldrI54gCENavZLrzRXPzc8XATR4roTNN1Z7TtF4oSxFwEvjVzLb1FTdFdViZBKYZlybuO4rCqsbEYa+iWhB1SKOzgqgtDxcbkmbSi0qHk6ixicSuxsfXRdjEmKRhNVBTsT198cEnfj4tH0jk89HuyLmBGryKznTwO9ZiyScSiWHgQZm8ZlRpjb6Uvv3OdMGj0tcOpG20vGChU9MDX05PuFU0/5SkIEtBH/xUHJMqqx6/D3zJFIeHoIn4OZWhuU0KkgrDoa5gqUmHdHewJL5OWYWjyWOIggvykX+MHMVBz1ydkDE/rFnpqeIisQuIn7CFNjz11VPLG3LbpJbG68xRnjKJ+coq5qtkMApnolLc0cywLR2tSueVInyCoCIVliGSTxje5KbyBr+nBqH4bh+rEaavcC/Eu9HSaIg7LuAqN5uwL9IPf/tzYMBJ/Xk1XUpQxnE11oqmIpHNceDOtJaJXCQBBBXZsBa/OmK6+riZyeS3vY7BKSQOk4ZQFXNpgEqZRWtxIaglAjLII/YnsRp0Mw2YBZ9Co5sIu1gyrkU1FG35nkXkTEWEqD4Ci6osjgjvabKzXmftEF5KNTaJUe0Hf7CKopiWABdnO1kbiPTqSTAZDUe9dRPdI/TV8kMPWqOLT3LtZCkqSpcpDj5zImj2J8qWroJUcFsvt/Lcfb45I6GLIDhWFxihQQfSZj8Lw7VuP3trL6izQaUUm/ZJGJ7BbjLw6H7Bao6HbY6Tz6YnX5n+zYvSey5KN2sQP9gXNj/9g4d+bDRqN1YrF8oRnESsjSNIgsQT0kccMZCQe6GthRXjHlJ/S0dQyVPNkaeUmKkXwvEkgUewwswkT6TtATtka/mqUJVhDOPGBTF5qzOmkV6JVpt4BR+TnAxmMB+RG5HsniKzWyauOZH1IcFLFIe9Sp1RDAzXPiGN3ZR5OVSibrU7bkYdMEGtfOQAEslcNZTiKcYGNsJ8zSnJyFRzBiTQzYvqiXtuenegOTkw4KQ5GTj2dROJVhAKGtMm5sDY1D0eyMEMKRcQ5UvEpaWpNUcksGq08KWugDNzZqC14AADYflbH/YVEmqOOLfQp3pJ5xCsJClNYHm9RsYkTeOFefObW4Iqpm/1LNioVopYJWuDVpwkoo76FGpKG01LbGbMACx8wdWJr9MifRhOk9F2AWHVvIxGUoSFBwMD5SSg2wREFvGghm355nWp56I+Zh9XHbg8uZJYMOanTwCwR6WvX5DuuC21IxEamv6jOJFTjlQGqusgEsC+EKcJMIxhY+1AgYNp22XpJ69NT/2BdPVZ6d7bH/WP7njyD3zl6iPm0Ea++KAT7UXHlg6i7MPvXKaBk6z3+BuhJcKKszji1eRpjqC8ltB9oBWTXjA5mN9keJl5ea3JXpFbzhBogJBkCEmAIG95y+FfAtBfSFuMlnCrCahiHDIqgFhd5AJS8a6hopJuGiccXn5p0dgQLubcc9kiPaLaWg1GKzdAkppEtQMpqoaKuSMrCVwYKj6q5D44DoMywrkf9TeiXHgr7ihF3ZiXcLJGkKsDe/3+DKuhV9SEy9hwnTwUaxkNXxscGHBSgyULvbFuGIW/w9zIKMpE4skW0mQ+hzxdaLM2TGYEHDEnqhpwEdFlm5Jl4kIEB1nWDLW57LLKhueQKo8wn6gK6UZFUWxrNxgscG1ec/QfW0ibhWJd+4uC9GklnGcCIfQdLEQDgZWUCk3pzkTCzHHZjntX7LxdP4KO9UIHZVUqTTOl6uEqP1fYh7D6Xe+qToEy47CafmVvEFIt2H86wouABo3XbJ966ua//ReHrrl5dMFdifnlGPCqRKfhW4AFQUjdIEkyXnhmPDJBJcvGFiVvuj492Yft4TU/l973xuLJsZeEjNWXCoA7YQ0ic7LYyWnNO/2L+VjESscsp/u86y0DAKpg7cZqUAl0hk6s6wQMgQjuE1weeTHAgTy9aO6EWSgX0X2hmd6Kd3HaB9Bh7LRogVcwnlhgTHLCwvJy5SVEgGsYEbVL/dVBDv6KncA3VTUSwlQM3/DVAp3apcKwtZHpUfyFeLzlo1B1duFdN5UewEi2UhLLSomRL6WbauWrhqs8MaWLVRXfMEQdfEwWr3jKGNYcb1hkKHLuM1NqJoHD43kyC/+qUxdEA05aECNPyGxMv5OZSC4Bzn4ygohBorv8jIMICUthRGZZmZFfhBEVUvKKlLEiJJKY3z0iwpr0trdVgq9GZKJsSXyHC9j4E7FiZDG/mJtrR0Sk1aTID1vPJnphNEdjacSItLB6tgAdTwEx/MWFNkY03qQXfcYRdWUfGQMYKPmOd1S7OPuAJBUWeytMpOymcUXk+84Mc4KUlrK26tDZSJ/aZWaoZKL23vnOShVRafTZjPDX2FI/BisFlLR9+9IznvGYU07/ma997LYblz90YBd7T36uLfa4OU4dpAhyKttEgleYFoA5DsQObuOtX2CE9TvSqKwqM5i5MP6hHJURi4knmfCK75WflLlInjS9zbxUePQdh5FXgBLwCBTAA2fE+3GVwDGQipwNUVwtq1HCO09rPMtF54uYs5EMtIVXdJa/slUNIEMguSEhhNQdcxZGYU8CRGB37YJjfLyO4eCRm+avNmoasSA9kKQIj+SACfKEpKE9uUmjyQpVZ3cC3sErIS68pSxfgUuJlSIlnsgKT6SXvwQMS+E+UweJIV0GaYBJv5sIzg9zs0kyh+Z9BlosBwactFh+TpebGZW3LptplgLmRszw6TJKlZuDNCRlTD9TywUJbrljyp20ZFe/ELEAScEEus2GaoCJTIz9g0QPCSjm3doL6wg1ZnmODADLgphGt4HLWrxJpJueCkFcPnXHhxJklo+VZatEK19Z1DUhS6OPI+tLOgmpD68H56BlNITklB22+kwa5bOqlujv2APFcRsIKX/NyesXIiRoQaCtmyie3/7tyiTTh6gTNgyBtzNgTaHZFL+f71AWvUg3Zy8JfBwdNLEOJlFz+U6v+8xOGsMZJrpNsEweRoxFbJ6vfvX2e+/dfuPWf/TXj175+20GbVgyGGO8wZJhamfS6T0Jsg9TSjO9HqeYZQ7NiA33tYOgBEaUIBXDT7YZp26GVUltGfCMKEPRLMMiSMKCQbZeDMJP8yvO2tB0it9ktK1dPrARSegjN0KsFR5lbh3Orvin5p56PadxRz7ghdIjNysfX9m03CccoB+tcBOpsElhnEipAqQxnORFd8AsX+XM2kSuBhYxfshw41OGy8vVI1lJiZkwvUy0yK6FQEjAk+5Fvqpe4Db8kVK/yEdVfdREhjE4XQRsCis1ICW9BKox0Hpy4CTWouvJ5rayzBPr6Te+sVqYIkPfLiGnt5VCsO299nsiBggjq/PwlJuQpB4lcTLPKGIrQjFInyDiCfpxYI3FGe8J8eeRm1a0thOKNyLUbFOiVoO4V4hOX7n5ayQUAAijd0uSmDTMxRF260lkaLmsL4u2UKZ/HSkOCFJFVrEGBpAkOEOwSEnEPQUGPn7pi6Or/0Ly0oWGj6CS3TjHyGmrW6xG9jRBYxR5NzTENDobPutDmEmPvu51adeuirezEehmD4RK0luWE3rZpMMuhxqIKptIeLX4rqQDxaWb8MKdOPYobSEnPIW4v2pheNJ3pl94dqV3Q93SowZt7awBph0xW/2JPZWQAVwo+ExUPlkBifqLIMLozZyg+4IccwqXkHkbA00lh5pyqlqiaF9Uvvk65OHsANXQIv0r5kyMuVnGCgWdeJGNUwdpuAyBqpik7hu67pQUj/IdT90JE45ec+0vqOHjQg7+qnCEKMlNKdAnMOTCWwK/WH9t04Nd2XhMZ3WwqMB8iYlWX9nALJ9iKIIsIBGk6D6oZEQhKd2PAWPZ4CsKi5SC1NDwg3jURLKovzgnk8Id81c+aqiqwQGls0YzXylx5vFf1WCgOTgw4KQ5mDffq/wyvCR52pulfATExO7d1SSZlqArW3VYpCg/ksWEZzCgHU9mCjlb4wCxZfEqEpMsJoPIJpwHmAh3Me8AUwZJ8SL95UzbJk4i3Dkp+DJyDwIfuoDoP15EidphJFCpSVSw1iHt9UGa7PzuGkhyHzdoAvr6Qx9a+h9/cQweql5LS2emu+9LxwTjsGUaeEadIvydSNA8i13oj4mJOTUuvTTNA5KiCAqJlvLhE9RGitMdc4TPiNLqJoqN+uc8WjDRkxFRIriabgRRWfyIgCOkhmHHasJfNgkDFUAB+/qTsaq9lDe/IXuPzmJGdQ5TeQgqBAPFYpFPTwICeLdZtoQAMtbCDXLm9TYBO0h4kH1kyMTRKXrErAQXoBMZQnLEIERluBoqai432apVZIs5QS583Iw70ngrsrVcDIjjEfzhkZyhQBegvA+UAzpzSvLFy4GNz3gDd3gM1R+5qRpWUEykqqQ7RHxiWhCzMWOzHFRbMnXzLmY6VMIdpXhFnl5nY9MQTUMMSBCPKWlxC6bbf6NKHkFmEnsLvjQQzGU1VB8/nwg5T0WqLYDSmlCtAEGSQbYDzcyBgXkzs27eF21Qyio258V8TXzMRsQfdU6AmhtmXajD2bJ6ZLxlSUc4htFbi4LbsBGlS0CTQcEif6FMaoOYFifRpHFLdstKIk/Ak83nRKeYG/67pkprZhiVoRiIs1yN1mQ9b8qHZCdz6SoZyrlGUB1pG4QPVsxwIUNGk8hWpjWr6jvvXKrOT2rQt6Q9NZxk/xq/AFb3JEY+pfQksMFgbq6kLe7FYwXOYzdlMerJeeXq8Vj6s6DAdhPdf/Suxf04oh2D+RLIbZzrHNtDkR+Tj+ZRmD7TEOwOJIEmFOq0ZKjzCLP8GSoEBcUM2Zd999M/XTWWaYeipeabMqq1RMYhy4aI0dYmNZyIg23IDZwUGVraGaJAg2mLpYYxUnrUwYXaegRPYqMPdKUII991IJLgsGT6118fTwMzedHuY/dN87Aia7K2A1JsS05RgVGZxKwQZMV+AxnbyopFxp6v0KzX1UrTfJQY5CmAZXBynzLBAjpMhDrTiLIYsBLTKKsI1iDZcjVqi5r4C/fY7WH4GcZyY9dk1TMmNQr5q3rQFVivztMSPoBlliKahgmaBrqxFzYn0bQ5n7TpB5x03Lre3ukmTRQuXqEFrdhYtk0wixvT0twLMqlmc9s1a/IIuCOMwOrZxl2CIxNBJuSIzLLgc588wjQSkCDDRgqvP5E7BBCbhKyY7uBUmUwkhUJUFAkJHhX4qZ+q9myTuTOTHS7/9b9WOMkiWB1qOImId85TIANFGDn8iQLjxpGgDcbOLZtHVulNqHRaqoev20voFSqhJ4V665lYKAydbTUP6mXCPVt+2HhoMsQuA/PRBNYeoWlyyokXfN9cUfRKB1nTU3WtRM/R94EqJGBgiMmoSmA0HQlz4I80oKGuMSY1BExxkdVta84dN6FDUdIzgKRQusYtXo0jjWUlhQwsDwwDNQdN+pC5w5eqdeaCETjRSmf8iwd36luYvQkuvlHYQqCSRyAOMAEtuYOMmVhRuI+wTltCWrrQItdmk95nIgJEoBZ1CA67HzlEAr2gjUwssjK69KCqvvvdFdYB9+EVCImVjuXJlOFMNFvhldapLX+PnvOcSqQoTg2hZHUIG5I7KiaBfHhTNdA5wHrNfASSGJOgNG2Hz+wzCEGknmrlYzkX7a2qPiUZHn67OkCeJpvpeKi9NMXMQ27KKjzSkveQ64+0Jp8o7TFJ6Ncagf/dRGbR8SJs4l2T0PrPZDapBqpxgLSyOYgYveyyyvxOWuEVcx1BbPlLtXtEn7EJ4SpTEGUs/UtfWsvmKMKIB+LA2JCIV6pa6AwB5NOTrFztOWJQca7ecvra2eme29OpP/+XF6387unE8WydSCUIlrICHo1G5OBodGhpaWk0OjqSOAvI60wqzy8wkVZWDm1bOrB/dNQNtPrK6OvpovJdIzagQH+cBEwY/PRQKLDIDW5gxLIJrna2OJVpS5e/DqbKJjH6jOEhE5MhpEi3fexjFSiZShnQ646NUKWyMjlnF3AP0NC6/DBsMHP37gpeh/WCSTITvSsoWM3ZMGjHyJ8O1heKM8wE5dCOMxBFaADPQAb8ROZoCLsIwcKXqvJ+2lb9qfw+pI0mlL/gggEZOKbjReHeVDhAyRWOmZxNjGQWgSyFOtTCw7RVYV3AiqNi4QszTcxZYIhlyNhTnIKC/8oCetzxgTZi/eMC6RoIRkM0yh24h8FMYtdhWLJwdQKq+2ZHYC/jimXIWlSCDtKh5fySEvoJ8JffMgcJH3NclVSDLNKK6AvOSiYfzjsGUaWrktw43XByNgL6WZLkiSdIcTfdVC0q3KnVc7b8T8K3jgrTk7Dxx7fJFmpNYv/oJjYDJ8uZwCaVD21t6SAMcKAmB4gJokGwBUxJquKVc5ZJXvqJEEduUmBwDxe+gwTF6xCUYQ8oc6Ohg4hpv8e1a1e1CBbEbUXIck7w9SQpubSApFPS/mema/w6+L3pLBL+0fdd/ysvv/22WwurV88cV5MJnYb5gCTfVv9s2jQ6eEbac8rmFTYqH0pIAC+tEERoUhUTybahA6NNp6TS4XTo0emWPekc5cTrOCx/wp0a608iYLCuJJmwcKhV60jGN+YiBjOIEAnja/UYhlLXudOSeQR4tRJlJpSHwamVQD0RQmpFrVK3q8w/mhB4AvvgDFAgP1JJkxcWdHJEqwQ4+v74K+N2IgRpfVtP9SQ9S78yc1oMALWu+2AsthzGUSZbM6XPvJAGEzAfG639gCTCDW7wl8XFVDVztZR2B5G5tlli1ASG4yuEogwbgAMmg2Ngu6XVH3+FhJSOz6otvR310CqOQVQe6VBvmf5QkQ5SAXBEtc16j4BsUFueTFmSyRPgYAQKfNaTdeOSaYWaG2wqXDrvlEJGwesGEqMjgMUxas2A4bORCa45ejAIxxBMiScDzcaB3vNmtuyHt8ZzwIqBgLAeJW2RyUnh0Wfd00OYpNnOgEEoIOZZ5nFWirzUru4OVHCg5Cd1JS6HlCQc3Sc9yQ5/iRJrWXI567Mig2qlG2kw3/5E/RV5epd54LWvrc727UPWzUxZ6Gnps3elR+1ID56R7ufx25PO2nn/TfffecbosadGt/bJLdLo/dXdScdgLEvWp6XP3Jy+c++pF0pmwIhPp+/td2PJMGbI0D5qzGYjrjeozljbkfYLfLoznVd64vBNPtRM69EJ41qB+RxPzC3hUtFkyphy5e6JpX/x4tF2ffD9Bz/9qU3XfmZzt7jXI4CI2VT2e5Fh+yUATTWCXzSiRtFSZqJolaxsWl8Dsm2bMGam7TW5cYvYrSaCeJytoiNbOhUC6PCcttbWTXZH6GEc6UpTgLGKW3N5uVo8UOfsr4ALMaXCLCvay7zH5tEkfJNAYoYomMCkMHEmUuBL3i4EyoicIxjtoPQTvxoItYApBrAxoy/0EcMeQBNmJCyC/HQT5GEB6dqd6PdAhK6NFohH3bxloEIqrsERdZMh5mtmWFl8jd7PaNIdBj+LEAgGQ9aOeMSs0DjEyRmoDpaah1h2RUxirPojeRqfPaMC5in3EfzugJOOW+eavXQ21fXWt1aCAGYiNGPGdtSJgEClXHYdNzveGh5lDrAeoRCFRKcP9SDmkVgJOdsEEE4KIGWEItm+zShF+IYA8pcMYjPwSh+tHB6B1fChraekg9tTtTnbnp7tad/+tP2nn/fAX391x/YdMMkU5BQiuqFJ96fTn7byVx+6+yc2bVpSPWkoHtLzP/yHKvJUPE0+Aqf5br5TmZTSdvusoaX7084SIeU0tBRzCzXWk/DZjjyHMgRI8pa6ibZhTmjYSI5p2B33bLrjb9zx6WIRsxkHB7sd4Nu/VtShaQgbgQiGRE/zWBgewO7WLpjIEL4zsIPmNvBMYfngQExt2t21Aca6QLvXRhcbiYBxI7kbMtYqwLMJibY6EKU0SDguMYGXTXMgDwPeEQ90tigrnyBIAibevbtKpvQ8WYAPB1wxhKg/KMaqdMUVleLvQzEZpcRPBiTYCE7SBZza0VJB6+w6ioOTHDoP4vDNqQlAAL2xneCeCx3HvWUlIIHqucPX5qnmqKeGwDokLcuKmajC6omxSjcmTWQY0U0fX4PiRTK553jo09hxaVQ+b6Mbl6bnfUZuK2fNjPFvSJukODlRufTM/yRMNuCk49npZqkAQJsd+hNh9+EPVzIipq4LmViODNSTA7QOIgEJdH9DIZGMvvpkEVnmJplACqqCDo5k+amvhDIh218GMcw8lLYKQs2ZcNocTFvvuXXfMbggP+68iNhMNazRvnQqNKE5uUX0LpzHbwhaQUt2VtI9fQha8mmmpGDoM7vKmRBoo57EAAYn1VCa+lNgk8jqeDJOkpXoY85NMd3UfA1hdBehRbWwku70IA7PFF+hAdDsgu53PeXlEUUuMIW+d23DAU+QOqgzA4aRSWfDCo5Y4qY00jLBOna2itCCBlBH0TSlLfdQIJTAnNlhdVYNY0O/MDeqA5scuxHYEZAlF62jVUl4ADcoQ6ZKajsdL06ZJzFzzw5EWr88QTPnULuABcsgLc3M2EtKUg6sZzwDjBTE3cbobkhrONHnI3Jc5I3Vi2WnFyEhM1HFXMsqej8ydF8OmgmPqjaGaKZr5BHToIkcYWoROeddY1JZGoIPG4iYG60TrIVMcOw1AASw4sxAM3NgwEkzs+74vCiMw/4IQQ/mPPJXADJL9UB9OEB0Co7hmrFsJfFxj6gNNeMv6TmOiE6rT9JfDkRnyN94MaINxr1Y3qf20DfTaTvSvr1p57Z0wE9kWrs693FrevhFrziD4J6WbBGKg79rL/5D+vZvpEfXTC8aKEaVDUMQsdWzkKxZqVLa7AoOAJt49HYuArusccXU290TrMuPFn5B31PztiKu6XFWhhCcxCdViz3v2RxqmBXNh3EIsGsOP+OTq5e2A4m4h0qK2DjMlEmIgvKpawPV2LAM27XrcAQPldlBMDRvFD9UjG2IB/gwWoBgVhwYAhAxcoT1sNOYC2Lb1ZlJDAqxeAPCMkiKUgSKwU8Mh9HXcrbRneHH7JNDkCqJzgGGzCkV9tRfdS7JpOCKkrmpCq/EHAEFgkxJrAgrFKuwyqgqcxFmQrHmpiJ0k6a56doIlEZuLMRhblEZuApUCuQUzncp1UTD7XME1IInZa1O8Gu9plOgSTW3uQ8rtGigmTkwvWCeuajhxUVwgFghgEgHAstMJi+ICUvAgXpyANBkTeHoYQYgUsnQiNYkzTuUNzFKqlpJi72gUXJi962tdUQfkpJP4Y47ttyfzuJrcyL0qkkrQUsPp60ve/Wpp5zSL6OiMBVgk6ClVuO44/XRqrFq033pmMPp1JlJiZ0A5lNhdh1/O5p8pBB51qxVVSkCjLCxwz5x5PXD/xUkvFQYEFhAq60DiW5hz1gLnETXgi8sEwAKLYulc5JAezSuOwxUW1yFwZWk33mg6HvgAHzhWwE1guQTkfJCofvvxNQQ2CJrUxdULCuLEau/gAwl8vRRvboSsomoIApYcT72DApOgkvcAUTUB8iwtTM2zUkAJ5k+ZhxLmLOFYCxIxR24xJ6yPCRYbWtHMET8pd40boFFa0LAJdeTaQ0aRoaiCgt6A++UDtXZE2fOugawoCspVU+vwUM+4XQjOaX3VfWgOqTJEvMbSsy8RNJuLGNS1YbVsaTaG7HmUf8T7e+Ak060HplQH7IANiL+QrKY5yQCMTRQTw5gF1cR8c0M4IBE0pzEJIsJ9w5itENs/k4e4h8R6EP4ysrBelbJPUlKgbSwwj+Mli9Kt56d7gVoRFvuTWe+4s3nfct5vX1Xx5bnx9vPPHPp9y8/dOWHD6yeA3VwFXjxlNWXkDQcXai9AJMDY3qApCipPrx4IjhZIM5Y3B9bnfZv1LAdgiCdorvJYDbIdUdRvYAAnrhXr8y43Cg8KnB+omiZQEQ3K5uDTN2AJCax1T2GlcJuHTnqSkmDUEUrJtRlXEr3I/y/9j454McKDaqwiIDv/MJuGtsMVHDMVGJBJLXxbFQLElIi0w5sxE4G3boGwqAxLQU+rCskwwRjiRcbvPBI6UAbwOErnkTzzRegx33yCuaI+siBiYvDC39cq7zaQrRAp8oDPTLJJIjNWZTiODVNAjnzIjEOAViMTBF7EInVxALSolFK7ALFDE5+TMNAbY06oEFNGFfU0Itu+gulaRQhkLPCBO1l0wL79PVAAwdwYMBJG28YhAwiZQaajQOAAt8laWvDEdEp9KFV1ZWZUwPitb3FjiIegvLwCjFKQwgydYpjGWORXySL7d+x14ya8S4fDU2g+1ZG225OF96Wzjnid9v2Xc/YtqMKKJqRhNzefdemP/7wKWCEsBbryVU80Z7h7t2VN4QymI0oJD8Vx7DfHyTRu5AohdcTJFFpzCefvjZd+Yf7H3yIsqrCkoCk1abVwd+4VmggiDMngQI2fMHTUJFro0X30b7ZeDNu5FDGEIy9WtBGT5LzOKjEh9JKjDc+CGNZWYw3YkElp1XwytWbUIhdY+xGLiA8Hj2tBpugGRgFP2Ub1hdVVRaGBOCwZvOKlnqF1UfbmfHkwAgEYkJIoNXycoXDpIdvpAGMZBJRBGruAicl8K77meKIfN694Iw0bG+ODxCWB4Qx0DIv5fQMVDCWOvAF6/o4ETs6CE9kHuCMSUzNZajOWq165bB0zcZsOk/Lw1zn4eKRx4EBJ22YPqXXrZOsh6wXs2jYMLU/wSpKCLLhM62T/qv76sfWD6vJUyCJLcQWKooEZiL36Q9Ef9jXI84UBqoRec0zxWrFTYOEy4gyoQZC4lNM+9PRLdoReaqg2Ug06+vfABtV69+ak6yZIZeHzwyketwQfjqXYusPkhQEVWBa3uDWUTTmYDjdRlH9ztuX3vTLW6676uu3ffLmv//Klg994Ulfe5iDudCiHRmtPgodOSlV13MIyVk+GW/1B5dUteO46HKodM5qgPX8qt0k9M2YnJkYh8RlM70ASYaroWtqwBzCyWEgzdeDXGZmDfQDIfmKFe648Ja/xob7bsYYDqekr9KDHWYNyw3jt/wlQ/pXX3Pq2ZzlVxFbd+HhG2STQVK0zlsGkkLFToncck4HeZhJDU1M85SRVeWjRaAkg5YR60XNUQGfqKdynQEBzwFwgJQWqacfKgn0mbMdLk5yDgw4aQMMAMsv0vaDH6xs1AQNm7OTD8mCgWbmAGWMgZwCwmVohVYiSUs0QKaHRcSS2vI0HpHLhK+zgxkPXJdExTLbkPWRkoD2upgSRQdUKhNTcmVZ5aOJ13JzqASVsKbEpGFXuYI0M3RM/+LoURuV+xNWGPO8IWeffcry45dHP/tYbfzp65Y+8pElRfs92vDCOO7IpFgjUqJudcRABkn9CzI89D5YgGP8WU4lFU3YTZrMyNEkWTleuXRFNdPMdkcDQSIORFZVKBbiZynUuaoBtcMNgendhx7AGuOWrQX0wXwAJcCfSYEgDHjIHWm8bkJJhnSWD6NUGNUk8MhXBNzIU0ECm1TDmQIGGEQlDeHm2En52MpuMaMCTVKQqloeCJkSmeTFkjCTQQumN1WhTHWQXs6yUjHOU7BSW1RAZWAmuB9SZCaUD4OxXQ7YPtDAgcyBASdlVpygF+SFY11IExckl6nOlO3CqcRsSxuIKEtYIRMhRRYfR7KU5C8TxvFrv9Z+ZB/Zmsm1hSapSoC6Lh+RwqQ/KVzDSbLVZSRyJA4NxC6lREvkkjjjLN/LPMunE68pudA9E1POnICyt81YPWcj/LFk70n4SbdRckFLmyp+uxYvwm7n+GY//kCl4S11SLG16lHpqc+cyeG8+v0zUGUOgbEggmv9yXiGd0Uiw8dagQAIEcE+LMG8Rf4aP2LanFTkA1sYFeCgo60ExLA/CdlmDmH8MNgocnE83sUNmCZsNjMPklorZGhLIGOMKoEOkIqcMZPbziTVfHeADNVQLtKDnoYlhtO5RgEldYfEcs5kUvhwubqJIdAVCs6Esccd3edXSswLEJxtieUMdAsSot5BMaHEjQFhTRyJq8vLVRyhCihRBaA9valHSB5tCZJMe52UAajxx4nmZgIcQho62H5yPhpw0one7wQBkUoAEaBkGWFEQAiRIQL8RvoGIq0Qo5MJzrOhndg6jgQqOVnEDmcilVuzRqXEx3/mAaoX/0EfMjf8bpSZZKIl3CyJdceyG1E5xLSPvkMktdODOBp4DVgHNd8ymidrHusgfbampGl+9m5OUE7F4kDJ0nF1xhPBT4ymJXldGJnYczY58EIvYCwVK8/WbM0RPVvbOVVm2HpNp2ImfSl6xpb75pBofStuqoZDtmzFb0X/zCcsTCWBROxhFLNdaVAy4vlCUQeGFkMLQsJ8VYJa3KG/heBAWlo3J2GdY9icAyk6B8qJD/iuFQH69ZQ0ub/cDFYHbGotXc1z+nwhpWtv+Uig5v4izXdf6yz5gBWZs+wCRoKKgtwpMzl898g/jyIf3Mig58jD6r8RYhSZZVzMijBhufCUHg2MV2RiXOEAxgoP9xlo4EArBwac1MqWE+gmU4G1l2lvhge5oG7JzQ1EQnN4/UuieqEHiCEAR/lona/x06qddrT7GtFzbO+UMQlLGXtKxJOw7ASca7xs0J7txxIg+sMOHScJ5d5xk2Kzqc27KAS6HCKBsFNRscJ7eRYo/uXlaps0a808BGNRqFNZPrqLo2DoSJYG1RZ1a5XvF9Hn6Saj1+sdai/XB/PtaXdKnkV/JhoRyLZdDt/CdAFDBNXy1E1YgfMQrU70tT9pLEsJUxAoFiaQ/u9Kydim0FaQ1JoPx1Dz/CotNSrYzPjCtNTw4BJSGV5LXwNB+o2X2HbXmm3PmxjoDDb9AjEolDefVUkR6u+aGQnhbY29EgMc0xLGZsp5uunaMIsLgEmh1oSqkaeSi1oFcj4uQBxTSS+DSq3EMmQHHGYyhQbKVH+LE+0FN7XdOFE6nKrJAw0c6ODAgJM6mHNCPGLJF1TIOEGUBJEdFkAWYRuImmHOKi/M+QQ5KNYud9KT2EUY7gdkYFPWC1ELQA9BHAdPYztdxUFAgcUuORrLVmSutJIodTElEIblbEZLek0y4aWENYA4MTK3zLD7WoVf+crqfG1NmJ8MM5HL1CFoS4vwdjHtAB/zkF1LAnsnErOKjXsANEZlZektxjmVES1EiR6hwrtT3VqK+ywH2K6PxDBN9SsQWMeG5FQtbS9KOVLapP861IkPBsOcxIXEjvWx1Z9i1Ra7KbnGEL2uUcCNcaUs/uKmp2mqorE3jCvego0MIQBCdyP5wxDKqmEUX60KOqiWvplSgjxEI7ESFcSLCvSYF/HUo7L3XbfmbLYKJGIEUuFW8qKnzEgCFcxcUlQ+5rUP2GRS46HxZhkw0MCBbg4MOKmbP8f/qRgLBgwWcqKE/CJKzHY6IE5XO/71e6TUgNoANTIxDwhr5ZJjN2Lb0AXs84hQ9oiMprxJeX4T8r1G8XuWHFU6i++GpUeXRRy0jhsn1muZTPUV4oSb7a0D0QKZTfV6mVg+EdZTbiMqE/S/xh8bnSj7972vZXs81r3qVVUCmkyhzgJgAGCZY10r1aTiRJBwCQGsq/qyhpCOqQ6IY5qInobt5N+f8M1vtnNFsXDMQLAys0Trpq3+uWGXmCGWJEo9zGn4wD9l1seiiNnDWLIxXljPnDhJniKi4Fccky3QoPKuASb4jA075IzKr/L8cCPK6/7tagU6OauYDlCaCSj+jEkJGvM0v2W6qUwO6YPjMQqSdoilVZYKd5Oe9bqQryB8k7MTm4w0kNpQn3MN0F368PSRwYEBJ53o/UhiXnppJTtEM9DNhBqhbNvUWhw0fKLzYh3rR5iSxT6tZAkORY2j7HxhBgj1Q72xUfnM470aV5z79Ggcsif4icvPOJmZnEmzEGJA4scUk8sUSv03SVCXYcyCwpXJqGC5j6WtHhAalDaVhtZu5rN6x/2jJiUWgqkgC60s6N5JV6tFjClhzG3jhGtMjDan2zykDhAeFxtLnvbingsKPkw4gRvk7xFIMYPFq1Y3aIPJDYQFywxOPi9WFhiRoQWEcuEvbih3fsqIJ1+UeQI9AYxUSe/DiLnJkpmABgmfOD91uFxNLh8opw9IkoO3ypUJBgKC4BEf+lSDpKzzcH2ycWDASRugx0EiERt2ZEQAgYBiyyMTfqDjzgErb1sR7fom4p2VBwmxiwhCslsKWNFT3CW0nQvGEhaR0AQUxsJJzixADF30q/1TWeVzZnEU+onTPjpPJV/2shmrJn9NZg+g+VTGrzvjTERx5Ryj4VETK3uqkT1JgDOLEfOPCDZ2IJqyRmxyNi70qb8XKdEytqmWVetXe/GED2eOtaYpbypCzaWnxcFKPlklQoScYtCG6ze8Ybq9gaCkd5kheaCgRoMKVsBGTdabDJaZb4yFvhpdgqjoe5Fkdv/VhpMX+S4xNsiQ0Kc16yDowG7HJGNL3eWXV78cHM2HmYLk6dOT54ffGfNPfWpU5qwIEyTAmYhAw4BxK84d4DXbvbsaEoEa/byJ6mEvSxjPY+2II/kwj+Gk18GgjIE0nNMW8gu7LzCq7SSqiTnQwIGeHBhwUk9GHedklIoPWVCTice5WvMVL76ywyozX97r8TbxLSqFQqLVwFaakpfEWdUCq1l3xB3HRmgCmkqDVCQjnWkmQSchtRdeS6joda+rFIkfV1EZbj46mx6CA+JHxMoSxRFzLIrNooHUULSHD2fEDGRkclqJ77EzS3HMEmKim8hDsiDDWGgI05dNXowo1BgGep0CA/J4OfMyAAa1NZLloyfZBErpTkX6UQW6SYUZ7WCLl7ykMiABgtS/BjplFFKxgGHmCc+glPaiij9jZOpDjCUaKKrakDBCsE5l2B3DokOpu4lF3EN0vCIYk3Slm5JxaIqi475klhMMp2juXaCHWVEvBLkptOvNb64YXhIOcw3rd+O22VNSgoPutz4q85nqOro11y3edVM1tE7TFMf7hs9ahyHmC8QDOqu/8xTC4Qj6uIkzmbxob7+4LoYxbznkkw3eX223BcF9ZjPQ1le9xpAmB18HGjjQkwNLqz+f2TPxBki2b9++Zz3rWR/4wAeWhdoOdMJwgPgjv0qyDfgnfuIYYVc+3RDXlumibi1wqTQIgOqiWthR/Bicr7QvdEKCA1JOiKEGiGYagqTmMmAPWDtSulL8pRgoIdeQEABR+uNYv8S30kOe+iCKJ9TYtBWjtmkpiIG1g8JjAtHqiWQ8GBUqWSN1ZiqwRdxByUgoPSdLQY0XqmeHDXTABHtMbaQV77ZfQiEsEDXTVy0pS6EAJiASl0JD2xkHYjJgNJvgXXEzICDA6kKLxpHxAECz6LCjGEUGCeMW7mmCvoCWRMXBQACN0WWkSc+0hm8+mSL/qIbr1voAHyr8/d+fXzp8oWiQogZc4plmxsCovzPrdy3K2CtGWsTSqXNAW602fkRemike6RFw01GfQdrlI3G0t6wFVjvsyiIBi3AG1rQaAR8dU4lYoQQt2CypUGOJxy2ilMochuuTjQN79+791m/91rvuuut0g2YSTbnympTd8HzgQCsHCL5W8d2a+ES7SXsJI7UfihpmK6K6iHUqxOkALkLEk92kPLuC04RJarpNSrYlQISXhNC3Jg75LjfbvKlDxw2vESko9FDkT+FxadnnbzMXbMQxoWJ2IOoUJGWZeKoqYYKGMI8xh9DumokDPWmcCcc4gXXs9+YEZAjpmaEmgxoMOdOCJLXlxbbZ3u/0QXitpCud9Zq3s6kh4CtupgQrtRfZ1aBhQwU8lZLzK3o/J8MrZ0kLu2H7YYvSTGBIBeQpJZVPxxszOAzEOIISRIO8OdSMn9pUKr+W17ksFwYwOGvIGZAlKa4VJEkTcHCBUElBmql1poymKTqwqTrjho5Toutogr/Gp2SZvFjjYTzitLUg0a5ILB8XGsuiGTjJtPXT1z4DDRyYjQMDTpqNb8NbG54DBHEoCYI1AwWinOym8klk0taC3lfGEuEj9JNkQlI4pyhORgiPpKdIskah+4lsasDr3F5OynY8EhVVinhLat4BGGvtcFJr33Bxcl74LIq03eEIfEbQiRYtUKFGDQFTnkFo41hinDlsUsLJiy8ebd68CcPtivrFX5zRaSh/reBKAyJbHXyAVIRpwzG0MnMF4GLwTCQAiKmDjUQkUBlPg1cCud761gp8y8dQFJkEWrkwhDDWtUZR84AFjKV0yl6clrEkzWzE0skqhlElgRcGedP+F4hk5rLKImrX8vQBjDAzSjF9Yh1liuGS5mOCEcWHaKJNpECZug9F5jKUuUbluTkxk0UluO46HpqwH46EKp5//nhb4qKKHPJZew4MOGnteTyUcOJxgBeD1hH7Qu3ZlcYrQRuR3bSXfU/UP8ENxzDDsP1QMNb3YrFJcG/RfMIj7OGSiQ/RTNnQamEMoLwBLArAfeEU9nx5hEKCE9wyIcdluNEJUhQcI/iDels4SAqOMVOJ2GXI+fKXS24d1j2U6G/91hJtGvwvU0x7rTu41URki+6CbkvSlUaINoJQNpnDUv5G5FmZrHkNVauYF1mMxIFlnGQkMJgJLha2bKgYV0YRBnIPgexAFeTk3YBNdgWKiAKSQAdMYFKamVQmrwdyJoJ4xOs4UaJGMVxrNxf11URTE1PMR7twWMPNi4CG5pEL3SogSaDSRBI3xo8GaJpWuB3TMLZQuLOe9PGPj/7lvxQXplRD9JDgxV//9c0Ey0AbnQMDTtroPTjUf2oOEM3M8kKL7DAK6QwScY7QVYJFhBMR3/SEwFg2IZtoCHGKChG7lq30KDDEpcK2RMNRb9a+7lBvJD49528Qce+aPwUaC7eUIigJQpyCVHRTbx1+84T/pyEceYxnmt/HsjJzg0DY17628mY2oQmcwTEnBmghpNMFlvFIOoxKf2Wiqh008KEPpfe8p/rhVQOjJ4WFw5ihwrPBxuv2xAnOExgEc8MukYzdyCNDwh2wzMBgJhHbJAaOEQgxXgrVmpmMQ6C/edqQwHn568eyyUqRXhf3b+y0FeOY1kaRIWaWuYNL5gUoaQOEa0xgQlM31xPJokUTjEZAVrbyiRyMTDPd5F0f+tM/PWRLMj/2keI2feADoy1bVt785qXHPrZHM468Nvw/ATkw4KQTsFOGKq0tB6gEag9UIqOJUViHniCsrfwgJ9ZyKhNRXfF7Eb5apEqAYALXgA4RTxxb9Vr3k87CoiUTLk360y4EtJREtk8E//qlT9fUDzeH5S9HnqOuaYINSkLCtYhmWlOQRFXDnTZq0YICdWvkUYCM2v2Zv8oQmHjTm6rwIyBMV+7aVcUR87vZ1DatLSdGkcFA/cfgUTEtYl4SCwVbI/jJ4EEBCAwPhRqBYsjAKRvjjavQ9F7MYKt6YUqy7csHOq9RtNFv8oh0htKCVEN98tfaKz2/yqQbZsmfGc8qAnNMDbjQckUlzY4wsKlbT3KknEMomHj1oDaam3j1539ebXlbtx21b33r6N57a3ho6fLLR6xig0mpZz+esMkGnHTCds1QsTXhANnN60Hvwis+iNWHmBZkI+4EhXpzQTM5Kpoac+ER8S1SmASXmHoOxQYwycR9S0kCWnr6FUEP1IAXgSr6NRwK/nrRWtlHYIoQpQ2Kk/BQkCz3E25068KKF3MQTYmHcAaFh2mQa0l2d4etpbw55zVI5LgpUeRgCk3PmOQOg9a0IClwRoBIji3aGqOYKiFydikotNPlDQAAQABJREFUky430rQIpEYGhgHjjtHFdETfy8Gx0TC9kDJ8VplIOUMD7fDyW8tceDJpkiIUzTjKkWq4ShNDt5lyqjsTB4aCIEKARru02lTSm1rqSAuzA2cAxKiwjbF26nXAJm/JxILEIPEK7mmImWg+Ms7FVJ2q8jMlbmNu5YBrvT9TCcNLx4kDA046Towfij1+HKAGCNbSIE8Ek9eELK1MvhO15KyDbXhMqC7pXdBtlrwW+pwX0kgZ8peAFm9r6U8i+z1d+tWLEngKOXEbUXtsTt6yelaQzD2VZ9ORdPxYMl3J2o4boINWrCnhdnSTcHixShAG1gUBT3YwwTELJ8Og/G04fedoqKnIeNDpBgaDpZoLMNL7zG8QgNA3UXHGhgSh3aNFWAqpQPBuGmzGiTMkgSpnSl1zTdVqw6ZpDYpagQX2FgiBAgve/vaKUSXBIoxhgGYrXABH4F0lgoPGvG4FWXzWgYBR0WBMsM7VZHFxKpUGvv711ZEQFie5o9WEWZcFruyUWvUwTeV1XGYRhGpGr6mxs1aH4esjmAMDTnoEd+7QtBYO0DdxYKDYo1h9UhUW+hQVZUaXMB0BNBQG8e2rdT8ihak9i/6Xv7wCB7b600ksSeJGKfJnPatayzIXOYqG0vI7cWR0hJfKXD6W7HSP1yO30AGK26CEh0jT8Gc2qBRgsbv5sIJjjUK7M+bZmc+9grfwJRVrEyKXzTqQqmavWZ/iaHTGDwdC6ndBbPAH5W08iEkS62ZoGXWcbu6gsGUqQjMNJO2FPoE/0Irut6su778zZgTc1CjYCJaBjKxrxpgBtnv3YaOmxGC6n2RxbmeG9bUcwDUlwiWqFIdYStCnd2r5zPAVl0Aff1UbH3S3HYu410RpAtj9gLTzM8c5sAwPTBM4qPlReRMT59moYvzMUL3pX4Eu6dOa9Yi10M1Bz07PzhPpjaH/TqTeGOqyLhwgVS2+nfsskJZuoCcCuDiYmEYMY0DsNiJ2SXDkwtoU0CGOLfT9FYfLbuSCx42/IJJRgbwbcqDzyH1YSsyEF2kppgKqUUwSdGX5zhEj9GSjE520RjoV9+i58kdU4E6hHg460lmUnwTrQ7pecLEfDmolzVcxMJpi1qcsT3bhqR7jItCDJEAQElwu4ph5Us0ljiFnYLiArY0fQwUWhxIMD5mAgxkkVVm0UWQCPdgS6MRLOw+c0C0TX5WrAsCTwB1HwwuL5k/kyFOBkpSrDjEFoqqeRrZa4SKuy1cWcr28XB1MavY5q5NpEIlS54lWmVZy1hSmjSONciKl3z/mDoZKQUyVF31vCK0bffSjW5/4xJUbbyxx0qF3vOOUH/3R9Rqp69bUk6+gASedfH1+0rcYGBKxQb3Z4U9YhyagJFxQeHwltlAx9TMC+UrghmohwcEgH1pNtKmgE+LYddYuwVenI3KgiH+iFOOwZmlYEfwFm/hcaFMJqF4pS+LUi5U0PaoOyp2BNEEmKuavfBQafqsZsup+Rc4cOuFd6k7Z5ykOR9t1ioZT3iwx4qnBypI8mootuoyZxO/H+YUcWtYRBmxgEAx3j+hsAKgP2DJaBHdDHq1nF6kPi5oK6+5//+8T9Z9rGAPDU8DFeRNgEPCtmXCz9NklJH99pJ4ZIkjvF136k3Y5ZtpYZQQF5dUBFlcQ2GE/IKubm364g1WGe44rE5ONPWk0H3ZnpjJsVEmFVT5Xw00Jol/6V2ZiSmNb6dilINOE3w32RdChGZdLn5hPLYE5i2lWL9yU+lop2GK2cucpax0IA2+4YfMFF+xdWRH2CC2tvOY1h372Z7etQ9FDEWvNgQEnrTWHh/yPMweIe26F8HxRA5QEBU9JcOKIzKBBUag0fyW2D44+o1oo7wAcpC2F4V2/pJEPvmMfopZCrHtRthQwIpQt3AEIK2CqC56wwBXo7T6NKNvdu6tfZqCuMlGlLFs//uNV0YivxDHNQrzZvaYilQe2IDzgj61LMy2pnQmkboslrLBSZ6uA/Cha5c5AwW1/A6xorH1tKkzJwQ3Umz6ah9QKsvmFX6j8NWUNucP8ooUgGKHNmhCldxSkhs5mtNuOAZLqNSSCDA+9r5L6l0XEljHRaaVK1q0GiR2UesSpS3CA9F4PXIWHijYk6FeDoelT66hS7REow45i+MkkwL1r2RqE8vcBGoxD3GAV44rCXuYlTBBUJ73ZYajoRyAyj2fpMU1Vo5tqJfrqvk+kbz4ddwfTZCtISw2ZXeWPP4GTVAz3TIRW8qLiusl2OX3BNsZ0p2Jy9rFWeeUrKyasD91yS95SONNCZ31qOZQyJQcGnDQlw4bkG4oDhDLnhfhfeEiENTHK4+aopDiV2JqepK6RVyg/DjXqxLvUNn1DRpPsUA4p7JG/wmX8YJbEQbJ1pjNDEeJQo3gsbbk8YBRxSDKUA9VFN/j985rU9otdHEyxUc7r9JmgDQGtv/zLkXffv2CZvdB+nIRxgp7juOFwEW5FI2rLYklDYBpmEoo2M2FiEaHqpI9XsAW2oMV9BVmY6PBtUaTh4qZrICln7te+dCiTFfPeRMJMqFp6Z1HK0HjQlSxMlLcjl1hHDCeYw/DIRE8zIL3znRVIiqPbA5C5DyH5KwfZuoZy9NScBOXodCAMJ6F8bUeGt8yBBjU3NtzE8IiKAyAgP803MCwDWO9Uz8CTLHrH16in8R+d1azhuPvNlHFH7yvO4I8mY5c7rHpBfIImjkhBT5v0wheOhfuqoe0aK3Ouc5BRc3QNsmww8Q1U53cMNHBgZg4MOGlm1g0vbgAOMPk4MxpOokLIfXLTBiLrZpiGL4wZAHLKS/mQ+zQEdeIvDeqC4KYXPYKN7NCGafyYhsNmYKBSTzjRmLIEbrxC+rt2YqF36UgiGxoLQwIAREHWyG/iZpAUj6TnKKHMar652ou1rxSkHVIUm3ZpLNI0lox/9a8O48JIz60jDS01D3jSTBYUATHW6wwSqORGFNT8C0wAQzS3qgrkCi7NU41mEfkOex6t2VErIcMsGQER8lvjLvBThXlLpddqQ4j9A0iSQ6vZD+BwsDuQpLsNAH/VxKACCzBfbvCKm4YHWAA2tZKB1FH/8hV56ojIxyv5QomqCgC5qd91ugt9p2gwzqh75jMr25IEURYQI5kqgR0qGchPDh3Up5I5jZzljyH+aju/qhjzIBCZMxR+bY3K8jN5rRjaCHeIgB4R6qQjHASqrABJsg3zknk04KSOHhweTeRAQ2ZPfGNIMHBg43CAhR90IJQpAAIUwSt2aDtIkMbyG66hpLM2koYFKOw9dAxHgHczSQZ2IKEezVWvoyOtXJmLgoAVu5DE1Ya1QEHCXKAE2fYhWlZ4eDdO4sqhiTmS5MlPR3NzhFGBAZIoSxUG78AFCo+nDPwC3WwL4g/STCt4VhzpZyNAk5XFLnSQLlBgZuO4DGnl3burCrNtsL1hb3TKuPQz31cThi6auIP0LO8kQNCzR3DV7nouVAgDdNC/PKQ0cSsBLoYZ3vKpAeveVQouMe3oNYNHDaF2pY9j2lSckUnTMhoV08wYqzJkdsJ8BlEwDn/AI1DPMAMy1FYC9UEMXaCG8eNdGzkBFy82SYsUOq7+ZfpIE8MyinDH6y9+cWWHy2R44E8rTlKfYIgXYSNGIxfGjylsIyGWIpUxsAMLRmJ3FFr6uHNZw8XAgf4cmFVG9i9hSDlw4PhxgKmDu41EDrmpIgQu4MLpwFbvQxOQpD7ErgUroCMBoxFFSFtYo9NtNbLklbhJVufEd8ZJChVdy/wgdltBrFMW7hSSH/ZyQR9MpLAKjEtGyf3u76Y/+INKSag/nOR4Amgs4BENASXQ0Bri9+k0mVWJvmREAfKQBDAWJAe6zUz2dVN1mGzJHrVVAcyBD7J6zpnrAqgCV+lpn7UmVhNIrsMWwt+XDW89K4PPOtRnImGCkaPJhhM9HZAIizRfrcAUvdM6inLOXgxIke/McKHHFaoaURP9Yjool4lUBwHQRqlNCdy+xirMwZvskUpKL1nsWjBymEulz6SXLTNk7qa29CEZWn6ogGwBejOCjccgNIyZFTUWu5QLhkbRZZ6K82KQkWY8M0kidYB0dbGu1BA5AH8+7stEQ8A76wE+4oEGDszDgSOjb548hncHDpyoHKCYqSWiMxPdY60vXJQeJZcJaHKWJqMJ3OEN4RIicG3D8RXOaFIAgub95h1LWwYb+kAcD4ACnME0xLctSBxwfkeMHhpHTDW7do17WCkbvj8BUtoSegX6YaRhf6J7oDGlaKPGapFm2gTE1ETN0yvapdVaIYHoDZHpfRR/a1Us6MWJi9Sxu9u+blqTPqPVZIiH7C7qmYkm5rdaH8ITUMwxS+DIuP6ybR5gxYq1IDxnfIKzdQf8rSNwG//xBx8YaZwV1EF4aJCwBWLjzIQJSowukCGCadTHgA9E4gLOBnecbeG0C2NV9FVAXi/qXGhenzLzCIV2mBOLqTROrXR+GAK5ZBjDr/q+GtwdxbmJcu/7quGgjL5QgbBOmVz2K5iezHJ6IcLJdZldCCUmk4/VC1zFkQr0QHKMxICRPK1MjGr+UJn7aroZeNYqrEpyk4lWy5nbXTg/cDbQwIHZODDgpNn4Nry1MThAV1EATq8hoOkGojMkewhQbaAPLHMpDB+hEmw/r3515ZlqdTR0t1ncEtBQI/nDKK95TaV+KKQga2hb7ejON76xku9wBrmfw6QijdCN2q74Mmc52PWtkqEhPNJAOIlyBcvYCegPd6zOwQX6QykqoEQxHNmrCERS5JbmM+Mk5aq57XsClmlcvyKMjfRraFlR5BCq3drMNvyV8FlriEnZrgVe4+qll1ZIV8wKVjRJL0OTa0H47DxSUBvmxnweT92kI5z/BCRxONbC0Wp10Fk+IHUNLuRknmYIkm82L6QBSvyVXpUCHLujd1RGp6uVUccxDbWDEToudsC5hmlinETcj61kAvKMN5MI/GVSlQ+UD8bhYZRSVilfyyeulW41Ij1kjwxUo8IjsMZPBrmGgVRS/tgFzWeC1YAeAYVaITelB66SQGIZAqDy9LoBJoFS/NU0zQnDEhc2QMx0OtDAgdk4MOCk2fg2vLUBOEB8U5NkJckLEEBIhDUhSwpTFZncJG2tQWk1C2veh24iheVJwWRlIL0MKR73a0SIUzCWwjWCJ+xNsxOeBmJlUdVf+qUKZASJWn3LWypxP460CwRRKOURFBdQCx+KQKg3vKFKYLHuvswtxykbQC22O8UrXnenFUOMK7f1vnoCZHYRsoExm/Fa0oI2ALKHUV2W8giT15mU6JQsaEmf1n7KI2qi7XgO3i3W0mBUMBcxBQXpU6DW+drOLuKmVCs90sENuh8/1Q0oKQdYyb1x98s0SpEsUsZfiC2yVXrYFKV3bSxlvGhAwt8GBgztLcgb2mBDsq4AZYKgN5FtnLZGF9aZOwg6kdgj5ap/ICf3ga0weQaOj8GGJ3CMogEjw1ICF1CXmyKyjVUjB9A3JXFDtc01ZakVfAm3xbvuy8ekllImamjUKdcHuZYgSCZ+exhoVreBBg7MwIEBJ83AtOGVDcABKoHEtwyFVAhK4UHksh80FbVKCoNKRDaBS6YT07SCJTWJ3wqSiFcpiXJEWANDMmQagavcJ8GZrOzGb1W3/D7CKVrJyhh+gpPU4ed+rvpMJHWmPikkVVIBX9UnpH8oRU0AUygndiNuESQ9lxxFQn9IzLTGTxF4TtCMa8BxIaQVvH4ccDVyfz2J7tS5mqxfmNZshqJuxxFLxjibzbhXOu7rAqX/yq8cBUmR2H31Adbp8qDoNfdrFAl0op5tPq0l7v4ar2O+4WEuxMiJAa/JornxJx4Z/BlSQNjsLnzQxq1Kgr/OuXbet5R77l558fP3fvHjXNFLS1u3n/rYc+5J27duXYJvpBQeZx7J2SyTm1EX80XRGqV0j1QJ2FIluMc4dF+2esfI9BTr3I/lh3cNYLX1FNyPvYEy8QpgpH+NcG/JXNEy9BfT7FGA/zySlbHNZmace0tuMgEHYT7VG2jgwLQcGHDStBwb0m8MDhCLInjCFUWSEpeW+Ew4zookdi3xiWZimoQl1p3iQ42NO8aGcGd4cHaOlWsERmCBDKElRxMJmOggyUjzVlKuVXgrhXBXPWLd3yCKgda/7LIKzBH9VIjKh6qQwCvIU+cguM8NQUl4nfuGknBB8SiRgtT2OOKPR4OjcB6nW1TsxPkLevohWHFgOlfvOKxSjHAo7NZKBi5pfTTtTSMNb51dyUDVJOdis2gaYzEUdQ0HnIj+GkUn6uiSDICZMZMMlRjjPAaSv8aMUYE/ul5ZTrJmfQny1HkW1gxGGuZwW/MS/u/27gRMkqLaF3jNDMuwyiI7yLApIgJy8QoqOKAgCAJPAeEqiwKioIAgiws6Kl4UBOU9FQQUREH0quwg4sIiIOpFVFaRTVBAFmEYZhiY5f1yzkySU1VdXd1dPd1dfc6XX3dWZGRkxD8i4/zjxIlIfOXvD87ccsI/nqgtO6O2uvY45oVZS98z+f+8d8ZW71jyuefGmFk2GsFCpCxZooBhDzN4QLOwFqvYtHa8HBMKA5J2iNw4V7qwHrlRiDyglWpQ5l2VQzHlwTkCFKI9q1aHx2nGUtDI3eL1VBaz56pDwY0lvCYGDF5hVlvznpzW5S0lEegTAsmT+gRXRh4xCOiaeeToRnWXBHXQZRvFUqL6UL0/E4tOWQ9rasaHLFoIbWHSxEDWR1j1xfpoPbh79cJ77NHivuKSAe6ECc2XOnM/KhfHlanIj4zRUtSqnLuXUSTIGeZ3yimFS7iO3qCZWooxN/1BTJ3IUkmblNH8hVE+/QEExgNK2o3OhXi0uSF2NayRWuoOwYNN9pWuP9Aw22KaJshH0zLa/Qh3HKBoGCqLhxabInLQVOTB4kTmGTWCTuHrQrQf1aTGMRLtChUoq6+aiAj9FveWrMsj2H40SOIv9uCntWzvetd8s7GyYaLNUZWjDnja56GZL8fXnkfbfBRncm2pSy+Y+pGPj99wk4X33LNosbzifN5OytqtgiBGKBG+rnQKq+Fprl5DcLmq0TpcilLLpDh+ao3yrKk7lz1MyCUoxYscZREz7FViSso5GqS1S1n6Xg38D8gScaMI3hSNH5MzhJAsr/DoE6oFzPNEoAUCyZNagJOXRjACOly9oS61FL2qrtnIHl3QsTrXsdKmflIhRHzhutc6mTSp6P19V4QONolmUCspbInHN4+T1oLo8I+x8XE1J24xCDbB0WjLoVooVGYhk3qE4ceX0h0y6WNYSBLGQxMQWZImH2qH/JthpK1pneA9/ioaDRGqiJoJVWSSxbk5PiP7btIWSsfgV5KkAqA5orrxAGvxsMM6wRTVYE8mvbrIPf2UrDViLJdcm6nknoTKV31MTRj5j39ceHGpBZYPt6gmdarVKYJ2FVSgp3TaDA+2IXLZGDQViXuWFmIOmknVQILXtgGANhbRWiT+5H2TX6ittngNDZRMwaxNeU2ZudAVl9denLOXOsKtaWmBhiLRrjzIO4KN4f2eDgF3Iiu4CzYTorCi+Rll14zFcRDn7nIQIVVYBPoZl1yNCTtFE2K04IlygpOhSm5UOvTU6+Mq0sxpXfG9OzA3cOqmV6BAKmVwEEieNDi4ZqpDjQD9x2rCRQmr0F/rQNkbdL76U+YZ7MFf3aVhqPEo/kFtGHRyOsaKSrdW+sOSNJ9TILrUMPVHPy6EAuhVwRhP40k6aJtulwyMhjZBQ383irU59L0H0R+EXuHQrSD2RpJ/fT39GkK7eLpo+JbMh7t0mR8n8qn4/uJeyii3yBmx9ofm6DINwaUGuWwUiJlbZDWxlJ3BI+oOOFypkEUVUSLWeG+vIVC1sl1Vqt+yVTS9C13WDpE2MS39U2vannrRGrXJ0OhujFprmkKfAsvMOJGmQ2sBhZ8YEn6mFQnEFTBpJ23ImDnsxZ8xM2oLWXE/szbuudrYT39m3NXXFMMAewcwlfkSMErKf8giSq+YGTclVUBsUkk15jD5uCQDGrn8yJW8BcUsc+vEIUIpzsURGFKeyLwUJI45xbkpPCB7rjj+sheGydBVIRY/IlKeji2ZXnR02YtQIpYnHUQgeVIHwcykhhECCAoiwjuBc4MelnnGuJNe5KAaK66pLqNM3EWviotQYwgEXw0KlQPQNdcUdhrmop12KgqFiFjlXnU90S/zWTZybT1n5176QxzWfnpRTpA2A1n7IDfVTzYXls+YaHOvTpyOsaOMOTIZplFomuj3ZUC/z1mVCNfv21dGSCTrJISuMsqnqxTB05XRXKH0u0loSjS00WIUZfShPXsT8JdHKC18Ew1o9oBQuQPEAZ82jav99CqsKTKpNvFyLZAtk7aWEyekrKxe0+lHBImjC5qcZ+ET2rymrj3Ij5bpZ1uyxHiL2OwtYPVkkCR3IR4vziq8rCRlayXkwysjTW+cnb2QSO+g1u4VU2RtTyvVaJ2Ir5U6F1K22CLBeRs+NQWkDHTiXgJPBwrlRmUMbqRoXjTnKld57TsgvrdDNNnwaCxNrjh9sz6Ko2GkJAKtEUie1BqfvDpSEdAb6rWpB/Md3EF8loEF3uhZF8nNk4rStyIchvLRsSon6ws3i099quajm2xL1AkvDb0/0fPq+hsFweqVJ7mL/cZBdNN0QwuhPom8hcSJIbK+npuU/Nj1h9INjcs24CNrElQQbqpmkUIbeUooFdoCk+OAZbKDUoeJ1dHG0EBoFDN3FnsTOm9kCQro6EnYb2wabsIUb0Y3VTcA8QMYDlCQHjYSdRRo95SaJkSv09N8t1WQupBbT8ckWt/YU4Jthkfj0TzUu2agPWtdWF00QjNl9k1oE4RTv7/8Thv/89HaCqgSvicD4UzFVoWQaza33FKMMaRm5loBgQzhaGYeWrITOCD6/kbehDtKgUYQoDKkp5PATeS43U9NnZ2M0RS2fno1cCMvjgGScQLrnYJ7BQwbdALxyvD6N/JJntQTyBleIpA8qYQiT7oNAYqBF07YkNhazMTpH8012IhZB6oT15/SXvpQnSzR7ZoZMY3CbYgXqkusO1yD2SF0x+VkXBUm/KlPIgOtxc40VI7HRUxqldBA9ArOd9RRNbNLvqdGDxHWEZsj04IyaRaJSrDTo3yGFhHBAJoytuYLCJFVnKnRhHDBBcVqdiNvOozAhzqJL9kVv4e9hImiRTYtyLLWySeQWTs6KJpNryTJ4/AkwNo8gr8z0yBxV9RgO7f3O8OaAeLiLdCWNANkWusy4ajlIwdIW3D3dtJ/zUYL/+aBVV+/1hPTZy80ubY0xz8zwwVZKgpQtDdtUisN8VCP0IbVizbpXfNmecscpf3Mff0TNwZoZSOPE88y5iHBnFQNNsxwKCfeAiTJ0znOhy+5aCIIMYhKSQR6RaC3brvXBDJCIjCMEaAh6AkMqeyXhRhQ8tThdRQflgptJ4L+HTdiVWKzodvcxexknTkXXRLEpa6seuE2xXcqpOlZptVayMc+VgyFcRrRHCwWHsHnw+4Dhr9YDn8pHxvhrqSLN3oWyHPWQjxkzt6Sl19ezCh5kCkPf+VZEXA+uoSusnRO4nVi22i2KIkESXLVuWlH5qWRIhgk/ddaGBRPO+0lBtk6cptXNS0LJ2EbTajpXRgqN3xgqiPg8+BGWQh1rkbcW7bMprf3NTBS81fVyxVk/NWqtSUbZWlFmLRVkzbCCGc1O3jJHmaMOX3rW0WWepLV1lz43udWPvfSl88cs+j02mKzxxRbVgRHccvHP17wdUSQgc0EsTdLwzOxFbPeSIwMmATXnhU8jvJBJQLlSXmp8SSeWD5XhPIuj5CH+Gmyz4ABE8L4GZINfrw4Xnw5KcWsHHxSEoFeERgCnvToo48efvjhG2+88fLLL/+zyqD1xRdfvOqqq7baaqt11llnl112+e1vfztzzlvr7/nnn7/pppsK32+//W699dZZMWTotXAZIRGYg4BhND1a9tFBCATqQ0m1z9WydLUYEnZC0zA+6Vh5LOlhbZXUKJYLtRaJMyFI54QTChOCFs2c4+hpnoiOMRdgY+vQcGwAlKuBMicbG1772omMMThZYFXNtnNcyiOoh5hlEI2SUAQHOmVDGkXgzmxuIsQtkQK1LUKdUCHBDuvCB/rTIwHKquCpnhE5GGiiRRlxRP5GpcpsmiS1zRe+36LZ0P0O2Y+MqyNO96wyyiSkfDrwkVQsap99Chi/8pVih3QhOLcVA9LBXcQRPw7575RExuKvluyhpmsRBeY0H2hDlTSkffctdo8UYmLazhQ+f4YZGxvwwt5557nTgk0J02KLjVn/1WN8go14RDwlcu4cG/Om2MoVyBL30JjjM/yIAgZBVGQ/6+7tU/Hrun/tHJgh2lQg7ylRU94IwwMzjBb3IbXeO/lUOixKHBtDpCQCvSIwZ76h11gdjTBu3LiNNtpo3XXXvThs0PMSf/DBBz/96U+/973vnThx4gUXXPDVr371y1/+8oQJE2655Zbjjjtu0qRJG2ywwdlnn33WWWcdc8wxa5T9/bzb838i0BMC3Ho4L+MfXCV0jmhKbBvDEQd7YLyJfhaP0YdSb37qx8UkmIr9J4UYdvuoAnUSE1j8MDCtU0/t6Zlzw807YDB1RMTAFxOSVKNQKpYOeTOYtXgfu10OQ68EW/LZWvnhYlUntK8EEbKYd6OJHVQIpS7z7E+0oHVzdCelHptPKq/EKbMFJPQbIMyBWXTkqeZEbQQZ+xPAnVpTBnUjT1Doo/BPxwCOP74wYDT9Son01Gmdim3zITA0KckUJH3CNGiTCI0KwvzDeD6ZDFU7JnpgKyZlrBD8wFBbZhuLv/i9sfzJoQqShyifZElwiDJjWl2VQxQx+igSlD4qAE6HWgYOooYPxQ6ckZ4HaRiuVsUboWEjFho2CuVdqBPmMQsa+OQxRKnMxqx6y9SqNZ7oo05aThSNxxK+Eu+UEDmM59YVtjG1uqfHT3dpxihRiDKWrE6NIK9qQRUYF/nLBGtKGnlSLtVhCyXL8TQ3YyFLKxwpiUCvCAwBT1phhRU+8IEP3HXXXb+hCiryi1/8YsUVV/zgBz+4+OKL41JHHXUU5vSKV7zizDPP3HzzzffmilnT9Kd87Wtfe+ihh6o8afbs2SxM/orAKBUnlYTzdLQjoPc0I0BbWMimgzYpwPZAk1FyCIdpLPxJuBaEWOAZiAg/bgxJf0qh65fPPbcYixuR88NAR3T93Dt8ha1XkT690lehBohH699Dj8oDFWtSo6dNw8U3tYQA0b7UBlVB6BJZxT345VDYUkPRsBQMzF+w0GSNM3HFnYMhkGUHQxbCsOD1ty+hL9FbdESRMi3TYEb9tkymomW6j4Jxfe97RVVylEFW6kTZ1Xuv03N1d/mpVeAESI/GEGKdo8M6L20A1TCBGztHRGQsjUGFE7GZLI3Khj1YlKlPrIKnjgyYElULUlOnKlcV+MsKqKY8y3mp9ec+r4//EBHgaQae7gSW2IA9V/EkIaV4VvVnGe7EWjBfbjZDh83XiTx7d7ANjMo8HfNYnUhW/pUF4ErqwPWDi7tXkyM9PbcuqRY/pSMRz4rHRUxvjUcoOD8kLzIiix0qfjilqSkjEGiDQgPkuqRDaCSCLR6al0YtAkPAk3rC+o477lhzzTWRJBEmTJjwspe97L777ttiiy3Yk97DjDtHVl555YUWWuiZZ55BhsZ4J+YI8nTDDTc8MKdrfOGFF/4Z7nxxLf+OSgQMMQ3iqRzqKtZ+Ux5UsKMqJrnMpnHOMHqmw6J71a1rQTp6A1MeFXQedmI2Aa/iLm1I2nQCrppsR85pAnk2IMZ19OZIG/UjUHevRJYvYR11ghtRG/gQLhVUJCKw1NDr0qHOqXy8LciW1Li3m5LzJrmxKlQOEtlJ8QBc4LLLCmQN7WlO2aKQeULNe5GLx6Fv7DN8eZBQ5e+jSAmbNAd35JHz2fCAxsjBKacfepFZgiWpJElljiTYSGhkwLQUhhRsVZ/EvuKvXEFb03KoIBO+Kgip9Ve4uxwQQmuofwiF1FVK+ejyJO6Kn9Byo1s0A4nEjK22zY5oCoxRM+yg5b2tT5AJM4ZIRlD2amSvFZIhWS1HtKaZRJLM95kmVkbFlyV3yV4Urcx203urz2p67q5o3mU6TpQ32otLqgxXZkbitI5+Gz+EeMG9Ar7wmJII9AmBQeFJyMoVV1xxO1tzg3zoQx/iltQQXAQ899xzy9BFc2TRRRddeOGFESCGosmTJy/Boj1HFvHdxTFjpk+fXuVJ4kybNk00UTx6Rgyl44b8O/oQsGaNb7Jhva7ZLJUpNhNkNGVTMb5kFaDMMA9jX10wXYVG6GoJ8qTz1enriFEW68LE5/U8h8w3Ta8zgbIRpiyOI0TeUAsaSy9vLZ4sYWxMXFURaE6EJkAFZN40R1WQKqu95J99q1RO8aJASXGwqOp747vxBx1UTWDA5zgFUw/iSaH5q2Ch6yRcZkhWHEDn9qza5vUGfXo2lICjymhH3y3BQhAX/JhBCH3phwCTX1GjlLmuXlJK1IEDGQOZ5qTNIBN6L81PhWIJGDwWDgPNTFYZmUwUluJ2gaKVDKzpUyK+NlmVkoII9CyPcJj80nKCOlQj93ruuaisG+WnqXi/mnXwRQUqrLy9/e1F2b/znaJpqYUSgTLbTlqUrulDq4HBigAViWj2Su1chlm80DjmIg3ewgi1EOLd4dCNLvdUqGr6eZ4IlAgMCk9CZZiFltQTNIgJtYawuQHiozvx4/nnn8d4hIwdO5ZhCWGKcIEY0vjx40tjkvCll1565513DufuqVOn1rk9zU09/40OBLgQHXNMoSOjO7YFs0GtXY4Qpp6ESkMjcBHiLlqNdtHnaoxaK42r6yfOhVAPNLgZnDbFZJBtjRrdmOYx/+bJUKWmpJjEqFX6j7rCMWx4w8hC+bFGcBChe0xhxRQGw9i4cbNXWujf69z+27Wm3TP22bdeU9uAh5UClQ8wqdVU0COZ5FDCY8MkEY9XeVPAfrGUpk+YE6gYDEjmnwi4FaxRaDlYi4nNMT71NwfuAxT6a8Mk6YHL3GJrwBvz0r8Q07LhFh1tRu2oO+oZH1KnAjUwrUhd0Ov0dx1REFm10uJiujeu1vGJaNguOeJcVsuTyLbbPUXDZk/VsA0hg0X1qVBavqNRlEXj9DE7b1Zd/kU2wT1xYnETI5ZVDmIqCCIuSxBwbzUndUUrbmtPPBdDkmaIn15YPyXOXMd7jD3J92QmTXqJJImJgceqw3RLag/mjDUXgUHhSabGJk6c+GZG2wZBcRrC5ga85jWvufDCCxEdHOuBBx4wucbXW1KbbbbZH+dtc/HII48wFyFGVZ7kHP0KBsbgFCc9PSXDuxsBnqrsSThN9KH6Tbrfx0AwiZiAayy+jhVFoLRMjpBQOZbG6FVNGZQayIlDNM4l7QsN7QPsNITZpFI8zkfcehKdvudSMDIvz3iME4Nyz6VxQ9gqlFFMf22qZI7sK8f+e8u/nPWGqVcvOnvKTbV1arX1UTu2mipVavpEiWBFbDBMZaYkoCHNDgudZsNsdJUlqR2RpwEP+XkC9cMbqTF3Ue9y1KswwKjl2KkLhjQ33oMZoLZhm1l00aIq1b7q0ySC49Yl60HaoRvFlIKnC/G3lDInYSLVPIRo7Y6wCIrs6fDzXOm4ip3O60HLZHo5wa649TTtrfkkoekm1BrFc5WULRNJkh8ufcYnTIe8/bw4kT3h8oYnlwVpTKedELdrViUy4HLuL/uZ9oxu6gfMmNcJExcSnjypDpb82RqBeYS8daw+XkVc8BV0p1HYh7hac9C+9957zZT9/e9/v/POO2PKbLvttnv88ce//vWvY0Xnnnvusssuy4lb/IMOOujmm2+20u13v/uddXA8uLkx9TFHGX20IMDJOvSEv45QHqbhqKuexFwVfqAf5ztCr+jlkRLTNA5GCH1xiC7YOdOLTp8u5P3AS5TFgmp0qYVw5rBzDxMXjxZ0RDdN0/CcaCp6f2YXViuMQl9PF9JVhuMOVgFGFsJxREdv3o0elSUGjC98duY2S/3hzc9d4ksST9eWXbl2/5K1Bvelps+bwwt5scikB6FKnSdJniuj8Go6T1OXK1A65IYlZHiI5oGzNgrDSZ2gI0yAaBAeAEZ4orn+sutoVIqlXalH7IHBspm1vUgvlD3AxFT72oOQoBRhaopW7dwjouEJiSNux3RVqKlGEGIzjHcOeWgUdE07xN7cXhU/NVSbGtSFi6MF8nVrSpJclR+N08Ag1nLKITslNLxZ2jNe6H2MVh2Pk76f5fncs8q/xgxULhangYxoTjyIZoAb9EiAU5ylJAIDQ2BQeFLrLPGzRn3e+c53XnnlldyVNtxww1/SYzVv9eonnngik9L2229/9913H3300bGozSYCJ5100sknn+wW83HuXVVPkJIINEOgaecYA9lm0YswvfmBBxbrrnTZJkQoM5sI+HjqBz5QuDiw4jgMfykbioeRlA7g4sAb2oonn1+1wSOLTtPnxhOlzyXPWhtOzByhLFRGR5qKvt6KLXNGrGLmTRySpQMc+JDsUWx8p+wSzgNGPkNooJtueGHszTctWpv2bG3JGbVxb6n9asPafTRF+RQpyEZTobC9T6W6ahpnoIHW17XjRaz8qoreZnmi7oaHwM0Unhqpin0W6vzDXJV3B6hLQQtQPqSWeRJhwrDZV5hqokLLaNUTlhgtRL0DQ0yHRByMQ3hAhMsSYw8CF4KNsaCwQgWrlgJDGoovshBwam9acqMg+uyasTFp2QA0cpYY5BvhaxTjDdbZniSI3e9/X6whjZGJzyNiyHLop2zLpIx5mwIl5SrFeRW6MrzxpDGmMkocD/PKQMmRkgh0EIF5ZL6DSfaWFGtQdXvJMroptm222eYmM/zzC5PSu+fI/MH5KxF4CQH9o5Vc6E7YLPSYumzdLlWhAzX0b736nZLQ1VJy7sI/aAKqhR8SB16fe2MK0jXzAubBjScdd1zxLMkKpMw8mlbgPDRvUeZLuaqeyYzEm4pE0CBZ5XbKg5lCpQIN2WPSJEohbxMmFIrWNCKP7DoxKfT81JkI1VixazV7JB01+/Pn1fa5tLYD5xBh9tNDBNEV1i+PK8Ulc3b05eCKbcLbFBnab7/CuwS+w0ZwFFXvaC0MRTiK3SxN/SiHCtUOrU63QBKtMfHIcYphSRNl8NAYHGH5KJN1u81CtTfWTQzJc03PmYQlah91xJLRYuaomIEVTfPDioRojbgo2NSvNoyXOOyYJURjhqhKqE4ZeyMs+/c9ZlxfozIhxf154sReyIrUtM+m3mWKTGRDW7WDhr2/4WCdnU/roGK2w+bh7qFyZZmjnIiPM5VZAle1ZRZpzTEXiRYpe0mlHHG8rUQx/RSO64vjKng9lFVJOJOYp3toVVztyGxsNc0873oExk3i6tZFwnvpu9/97m677VYuneuiwmVRmiNA2WjF7DpUSCm6XQdVZLbLBsQYRgsxgNaP63BFM4ymbzhVWD4mZYdAc3AWzujuxeTBGoNjCUYnTl2xATRuNtPiieUleo4itByedrQ4KB5HFdEE5kooWr2/gtCatCyFRwFw364TzGeV2lOb1W72XbiZc9ySKKC31n51V23Dfyw8wS30qxxaxYZ7GW2jZQ4llWfq3xJu+thnsMyPKBqrFW3XSVHI9qkSHkHFIQKhHjuZj8FNK0w+5mQ53atHUKMFbJO2vY4pMHsOqWu1SaJ9Rvvx0wmahc7i+uqddtd0xUEjYs6Odg/2I2WtQhNVp0KI5qfu3CURdc2vzmYW6lEKoqluLE1jVqeSIqw7GsOHP1xMkLlFY2OO0raZvvxsLQxCbGMGBo3i3iBJ8kD8lXn2KpWv4blqdOEN1dIsy9H8RBYn0Cjbm2hxRPrOvZVENLcQt1QPIe4VzVXIuIR7KRFOiSMqr6cDh4hj0txbbC9yIj5w5Ad6EodSmYfickq3I2BNmHkq01Z8hHot65w22GusjJAIDGMEKBUOPY1iWGmCjJlHj9larr66UDnldG6M+22jx6ijM9XDEvqPYSCIS4SUaUY3Xf5s/0QnbsjLPmS+zzklypJksK6jlwdj3xgfU07UJM0nJtrnvZaNqhhU/37MG/8y7rcbzPjDorUX2DLorF/Vdvj9uM3ljQ1AKRAV59S2YhrQ07L4HwVGm9LfphF99Z1QM8xa5pWcdExwWAuNGr1qmz5A+WUId5OzkSYsc9inOsVaTLai14w0yqEp+g5uo/O+qjSpF1Nm5nk1VDVrIwMVxBkB3Qleq+K4tVHnqBiurKlgtAK1CqRHVWIeGoynuF0rIriRZHlVI9+u8lgy/ceqJGOsLx6Bukmhr6IsSBUjWaNjOI5S2nuqyZom5upkSwtjDxQfeVIK6Yivqv3VPnGU8l4UR4g8B4WSVJzILc6EDBEsx0/3+iuytipO3GKfBY35mmuKrQGYfn14jiXVg7xNNj+DA/EsbFwr029Iyr4h8WETg5OURKARgeRJjZhkyAhDwC7bNFOjmET4whfaUgbGlNXRZPTaPrBhOiNmNHSsqAwHI7vC+KoDVaSDjlv0szTihAmNz+89hOeviTD8QQrSYQkwwNXpU3gUpM6dFmRmECKHQnh+UIT4jczML2OmrLDKhS875PFp16z81L3PTht72+xXXVLbzteuOClRJw738hCnnyRIYdDKIUxZFAmVHEKNmXDkjtPrNNPcG9r5R7/ZT9/eNW0KfirHI1DobFzEJKzKonTLkarq488U5p9qsYTYEhOFYmsxYcfjTTPAhJh/sH/MWMNAa1Q6mk60hOAEziEkXMtUp5iHx/mrzQBbIvgE3iAOQoCj4t8Ob4Rhg/aj9bYvKAiWFl+0xca0fwMGVhmJRzoeF1SmaaWxrvE343hntQS/Om+NIhDhbEt+yph2HrZM4VKTTplUnMS7Bk+AuJGAuoQXjHLigBX+569S22GShUzi55xTMEtDBRDJp6R8QwZntYeIRIjdQ7yJtiDH/1ISgUYEkic1YpIhXYJAdJ3tFMbiIEyL1omeV6/q0J/qcKODpnsMcxEaHbrdWSxYM4jXoRNaihUBf+qruF2CumxqQyIOlh4ECFui/OhIylUGKDz8BjOjepWIvpQ3J6UiiefOGjvumRXW+e1Sa1AIf717zF+fWsZAvVZZOM26YBpIylI4++xCJVAb7FXcU0qSVBbBNys6yZNkl+sNpQRNmqqFUo0cmKkKDVZmaESdyLt50lLsM+SDfeZV66osIgCDOROTYM8LYDQ2M0TaA07gFvWuMag4okk4kCGNB6hOtBbUljjXMMR3Ih0L04Dtp1YtKXXtRpZCgcwnSImm5SlRLcX9PYgUsDdGKfmXMo7FLqV0BgYSkSzRRLEQRlBtzHtRJ1IIgohjxVZSXiXxFVNmYOUur5XsReuIyO5SQOk7IOAnidcQHVRAs8PKpQGLr+DS8R5Jnz3PuUvaOXMaDJm+VIFG7imaPQufsiBSSiHPBER4kkDWpggpQlMSgXkIJE+ah0T+H5kI6F4bu+a+FgXL8bWxcnzpdgN6MxSlRDet/9Vr0xN6fFQDW9LVmtozUA57EqXFko/6iKZH5vqtv24hFAz9J3IInkTnKQ69SEl4KHViHO+5oSqC6zj33FA5dFWIQTblMXudRZZd9uXTl67V7IMwR7VEzmVDnj2LKqIglVRW5dldPsrRKDQiy5axeGdEYaggD5ZFGp5ekg9/mwpFZ1pIhK4QhiLmnNgNq7FAYUdhU0TT1VRUumpiTIq5VyCpC0dcFQEtCIIOIbTeMkyghngXQvAJh2hMSm7BS2Avvp+MK3iPu7Q6lqHYklHkngTbMI11/fVFOlLQtnEmn+CzoYB0NNRoYJqoCUSrHOzyGiFlguozljgwzWoFAEFuvFyaMZHJsCTJkiLLpzavjFosJuQFKVr1nBk9GZB/0fwUTf69LFFM6QANGiYi5SREfAdjM9Iv22K6UUEUmXnMc8t30yVFY8TEt5InzYUv/1UQ6Pn9qETK00Rg2CKgd7NUR+9Gl1RF38d80qbgHNa1GdbrxPWYRpxmvixQMujUmep59dc6ZT4frPo4CqrEBEUtuWqETRMQpiD+Qzri4DTM/npki/zlpKl4kBtdpRh08X46pyH05hSbEF25jptSkQFjaM81+qchMB4DZYHVIruLvpGIrIaa8VDKI0Q4SoT60BywcohGhMckyNx48/5J3IM6Jh7Dc9jKOk5Sofc8XlZkuk7E/NznahicqyNZtBbVitfS0/FRu8bSKKJJN1DzRoJElNjfQEXlqnrNIGoqrqJNrkZkN/qprhtFBBnQfoDtRvSCPdKJFovZaHWak7Zhhwt4a8BGBT2JaVmsWsuMZuyvlBmrTGlpOVhLSDzFwOCAA2pnnjlfYqxWvH+IVm2bALwfLwSO/GvJ3iDnyqhNGifgUlop646C40ny6XHMrtGSo+yS8orJeZQOYfJemMITKDLjq8O51ODz4x8XflG6Ao8T34vjxfQqEamFCIcVzioPKYlAIwI9dOGNETMkEWhA4PbbZv/gB7PfufOYN7xhyLQaHaDrZ7zR/9IrpfB3NrRtU/Ty+n3ephYcOddd0ge6TgvpY2tHnSwdYFfJYA86d6q8KtQVhmSEHf27LphOog/sl4NRNYqrNp9klDLS1U1LllcKoQ88i0aRDU+RDarIucchc0Kci0xFyRhlUCfIEN0g5zS0nNAl3FepEGyShlAuGTOUp41oEYL5ffaztf32q0umuJeK6qTIh+/TyoGNCFWYQgqhIatlYL9Ckvbaq9PP7mQ56tJCZFkQ6XV+57yw4QZeDQkvofupZ7CXbLXuXsYVy/K1BFVWFVUcKtyNqjLOARaHEBGkDC3VDUKiokWLmH4GgfAzkrLgi/lQING0sATxZZWwidobswVPwlpUkUeHSNC5Cgwa56ek/JU4fz6rQY03nLM5xdMxpLPOKh5KRDP8YHMywyX/2oKcCFQKQx3lgpVcmSmDnofKc7RYOAh3Lo7HEa9bFMeLoDFLzUvBwcvAxo0BkZfRjRLHC704xL1aNTR0DqqJ4RaGMqA4Hmo60iNSEoFGBJInNWKSIb0j8OC9M3Zd/96lZz317OwlrvvS1Kljl/rJHeutuV7vCyx7T7qPMXR8RqXUlbma0BOIBXrxla8U49H2RXcpqSo5YP4wMWFChKrg+2ztkhGnaE2FLcewW0ccFEccHbdAjquNPEn3bbYrNEqkRttRV8cfXzzOUYqChGJgcjAXQ1swaOnTTXyYO6BvGsXCInyR1zZtQWiF6P3pFc8FkTT9tUeUvQfpGMuXFFP6VWnceqB6tT/ntCsegRhSUOwD9DNeiSBgf6Z2oIaiMtOpgFIn9+cxC/Qe6hzFpJ6VQ8YZS44+eu6W65EP3L2FYMa4ooOal1QQC/GjGTtRcWofJ1CVUMEJnIR5CfF1RNuIR5R3xU8UxC3albzFJU1XG/CmMLpoCUiJ1GTbSKOFyJvKwYqi5UvKXXG71LSlCPdXykyqWpSNM8ptOeUwOEo8wk+FUhBH1LPMeL8kJVkURwpojTL6Kefx11XxSxLjp8eZYhMT0ZGmn14370VgIsPeFIng5AKRs1Lk36Gv4GvPldsoxU/mKCsyedNHWcrIeZIIBALJk7Il9BmBaVNnbbvuvYvWpj9SW0539OjsZRee9eI7XvnXG5569TLLzht49jnVft7ALmLrF2uPdbg6fUpFt2trO9b+AYr+FylhG2hHoqOviynQmNVkHOpDi8inmTjTHDI5adLcAXF0zTprgfp033kwKKchdPSEQir1nJ/iMF1QAyxb1o3bk9WNVaF7jMLxMwom9JAUEEcahV6RB/c6p41sJONxWIpNOLEiO2rG9BC1jcD423lRVDqTInVQU/vv3/lHLJAUQUoHqxdrBlUB/Q1VVWOzdUvNgd+mMEFR4RgAfohRsWrU1aYHoeYioDvOWZ60qKqUP8uTuCodqWnAEQ54h/bgp3RiSb8W4pBzBF3KWkVTwchNlmpsprewIoTJCSspLo6pR+uNG53DBDIRsy41z8LMTIGxg8qeEnmiE+k4197c7hUWTQP2IPRdbqUmw0L8FTkeJw7MESOgeR1QJW3bXJsCKnI5mPEGqRQ36hBEdq8U5F/KYNc/eB9xXKnxtfIupCQCPSGQPKknZDK8RwTuvmPmcrWnfEfM12NZwS3ytXvwy2rPnvedFw45srOzNT3mobzAmOQbIxgAR2zjUR2on4cdNl8PXkYevBO2K/vvseLoiOVBjyxLhN3EWifdN6FX+D/ZcFmvHVOE0e8X1+YIRkUn4SvUEp1B9PJENJqD2pCsvx5BJVBg1jNLsyosQ9QDkw39R8eILyZNQNfKnnOB5jhoJhNzDjFNi9hphg2gOu6vppnnJQLwpHS1NK5sLIWxa5c6Ujtgd9I+STKR6lM2lDphhdKMmTdUjaSIBqPSqXl0x4mfUfXFtbZFU3GvG4nsaZbBG/Bm1iYhRMPgr2MvA2bIaKVFaEVQik98oriXuVTj8dOqBWsXWAa1Uo+Iu+LEGEDbbhTIIGdnnFH8lYgMeDuwZcnCUx6gocUqrGbPtIa1aK4yT7R2RWAoIpqoc2wMFZOCl44xDIuSiNdBsqLFXSI7kTdGO5PL6FTgIIf2DPM4AnyTgCmJQK8IJE/qFaKMUI/AW17/3Bq1JRetPT9H+xdX7dsypbbEf3/8qUOOXK0+9uD/1nfb0EVHiXwYmBodhrIZ/Ce/9IToc02NOWgC1ITm4PPEsO8StUT075QrcmOzRz0+pVUnOnTa4pRTis1mjLyloH+PD/no9LEcImU2KvqAh7jpAxFKLoUkCWQeoNJoQYe7qAf0kfqhSyROlxh5yw+FwfxG37AloHcURjhI1WVpxP9kvnCAHhbIiEnQwLFfBQMdfqB2wvM60lBNFHxfm1xUUKSALtjCSuK2NmVlkdNwPArV7q9HCKT1nbQvbgmRAlYBg0hQoFaE0OAZWgKu7Lsl7LLeo6alwKLAhhWh19zx+QKCcOLEwkrKE1zz8yAZ42vlHWwqisNmiSSpAU1OI7RKHwJ8/oSYGpYTpdMgZUx+yiqSYdkm8qkZe7NcMk3m9WFG0p41bAf8kU4p21vBaxUtX2QZs+O8rZ7MUHtTPEU0080AT0kE2kcgeVL7WGXMuQi8Y8eF7rpc9/hS4+FaOq42Y7ElhsA/KfKkx2x0A+pIhRnioywGtTpxFvuwyjRNmfuNb0FYH8T9Rk+NtWAzWIvhcohAvTzVwlPZVIuPP5R6SwRd/0EHFZ0+LkVpIXwIlvPYBqZ8olukQ/FYyEPZSDAUFQXA4clA2daaVAXtIkGaI5QlnaEgIeLz21UcQjmJRs1QXV0o3Hp9MA9SYAUEx2lr9EGv2P0Smh450B6qFSclPyHcJ1HFZnVD3M5GxY6Iy2IJagRdcFRFnLqHVq82Pa/Gdx7tJGpcs9HAkA9PxM8sONAG7C+K67jUKFoL76uq8N6TQ7YoJBu05rDsIoY8NQoK5UXA3T0uWh0uqH366wPSIDUwYBbCXWAYlk50zSSd1ivPGrnI8d6hSs5RK7TPa+W50nTIiddEy2fnc8QIRJG9FEYO+F8YkBrzliGJQDsIvKTq2omdcRIBCBx3wmL7XD4Tc7DdnW9T8uZ8oTZ+ydqUw09ZpsvwYcsxG3LNNQWN0K1TA6gMK05TXaJzx9VwKRYa5wav1I+unJ4Ivazjdk4f8Irg4YEDha8G0CRudZ6RLrVuIsxUgsi8Lmh2rChUpqQEOhAynhmnnlqon1KoDQqDknDJLRQJHYPeUbpOQjxdUtKhrvwlQignE3Du7TYxB2aFum2wQUb8ZQWimRW1qT5vo/wYMLoJtPZFe6DaOYTViXDVHSJZX9hgiZRHFeSILLsa1aS9VQPrkmrzp6QMJzRLZldZwpBwEfRI41QiRA2n52+1BdEAADzCSURBVLWN37cjsmQCDj1CUORWy5d4U3FVa8RdygGDaJoo7uoWxipf87OqQGPG2LRYecCQNEu5kk9/cUrWo3jpJMUy5EatWtsmARrapxlbUmnZGurmRllCiRnAAsOmecvARKAdBJIntYNSxpkPgVdtMPaI/7vGSYc+YpUutjS2NnPx2rS3H7Da+/btquZEi3zjG7Mv+eGMhWY+Z5XYY1MXv/jisfp3vs8m1HoSFCQGzbpvjrqcMFgIjIBpI/zJX7MM1uJRCQLNfWBCVEWsp7voosJ7SRcfKoHRyC2G+FSFc6LHp1/NVvCMCZIUOsCz6DnJiknHSNNTqMBgSB6KnMk5kbixOxcQQs1QS/KA+VFO3SbYKLdq0FQFzWFe6i9Pgi19X5dkNfk49wUMfmaiMbpYxgdw+pvhpBQ16Hu35SSXqViZpfuRITXir3okKldL8NchpNfnlun3dCJl+ZcUFqJFaQBKFI71MoPM2Q+zTZ4Uj9CGtd7WovlhOZ6CuGqHURYNW6tjMfVcaxewN5yWuSicloDgJeJIhNOqLuHxRniQDDs0Wq+V0YVwJigMzFaxHmQI4d00Ma2kZpbjvWudvbyaCPSKQFcptl5LmxE6gsC4cWPedcAKY8aP/+yhz9Wef3b6mGW++M0ltt1lqYUXmWOj6MgzhkEip39t+g0XTl1zxsNL1Kba1Wjyi8s89uxq11+/GINNC55UZpw+EI1vhB2QddyUnG5dF8+oYeyrT9eJU1omBegDMcX/9reLENrLOXGL0TMtyyaCGLE30GEi07j2wCQRLU7caEZPanQGreNZSJJ7GQlc8twQJ9L0dBqLNvJcCVLnZYS58brgH0XKUFMnMLUsjc2h/L5dXYSWPy2SYgTCUFtTlmOPre23X5EQ9umgs010qr5Pf7pA3qZcZtxwWYIuMytiJ2oKJ1ALeIOKY2txl6f4GxJPdLuT1k+fe0Ozf1qCxBGyaDkIk6YoM54rxKPrlgU0S6PPYVJmEDJtZ1Ml0HkKss72Yyl+cHdtVWtUNMwm8oYkYflutMe3lRD26pRVEQigNHLs0wQow5JwJIxfuSpVNJGNASCJMHkLoph9znHekAjMj0DypPnxyF/tITB+sbH/Z++XbbndkrXZK8waM3aFFceFEaW9u0dArCcen/3CH/53yalLL1GzvcyYhWsvrlh7ZMys2c/8a62pU9t9a6gfw1x+5awYVBRlQEeiRzEBQdvp5Tm7mAeJjf5Ko1EAFL08dU83G7UH3TGSNluHS1VFUiLTOnQJzeovfUMwIQ+lliKCECeu8vbgjOwqjSLBLqu7ucgoFQ3cuALNhJOJGVtHuNpHoX3RSnyUFu9JwEtzO0rBDBBcJsNYXWU6Nawj6pFXuFld+j5cczQMbcZVVRMMSX2Vooolpc0gBP0Wt2tIDDlyKAN+Eil7kDxwkR4M0cZMruFk/JlQJe3ZotTSoRBxN9GsvKJFm1dMIMiehW9A49vk1QAO0xFDFLYqtxZpiizPuD4rFOF7xiEdkfIimG5jsbOxfkDdulDQ1iJiet2EuHdWViMnrW/Mq6MEgXZ7/FECRxazfQQo4NXX5PPZzO2z/VSGa8zzTn1szOW/fHlt4uO1ZZaoTQttNb42demVXlx66XbfGl0tlHAgXTkxt4KyBEny01X6QNdvWiGEKuV5THXRtSR0JJql32cW+vKXC1uUMbfdhE0c2TJKBIlENJrPkB0HMIkTNgP62Am9bk/LIEwiy4DEbb9kiN+1QnmahuQdhpM28iTFNsfjU7t9NympCFYQyt60Wk9CPTcV1LaKOXJgw2iVhRxQ/IgXG5KDqCzV1CgqWsmiuhuvtg6JxhYES5vU0piypAYeph3tEFnXMrGZwRBP9xR+4hyGotGiLwJDIOPRmr2jpDUQkE/hWjXGa7mcwQaO5S5rSH1yDhM65JBi99e4hbHKV36RP9RKMW3AwQprirzXVZxAYGHkhug1lDhPKUsxuIR7fVISgUBgTn+cYCQCIwoBY0qdoE7NYYhPaVEtnZWlZjyxYe3219b+zFPClgcv1hZ6trakNWQ7bf1vI84+iUyiJg6jXp04YhSqzl8dup69tGtwTvJToLG1w4m5A3tBcSf3/Qfr46gK0w06ca4kMVUXSTFC0EBG3tZFG0/TwdQeB1uPE9lWmSLQwS5RKmgWj5CuFYwSH7EcPFRr03KGvaLppd4CaW7bOoT9L+KqWfA6GCFMt/ESa0eofPOnKAIS4N4JE4qqUeOah789SVR3T1dbhLsxOJYTlBpI5oK1TIHm2hzmBz/5ycKjbvAEUIBXZH89uhQIsDB5O7TPaPxO0EeDAZi4iioxvKFE2jMrFJKH/fDAu/DCwuZEvP52KRMzLFIewVAKXvOuYZYrn9V4wpRl5YT8sKU5ZINtSZfS642NSWVItyLQ7si4W8uf5RpxCBgBIwSchEK4fehGJ00qPimql+yUPD9uqWm1JXesXWJ92F9qm0ytLb5y7Yl1a/e8YfXXvvzl/TTFGKFaO82WYfWcsbLpD32xsvC3CGFXsBdffN6cPqMMDj640ArWNrM3oFN6c+G0gsNgmrYztqZLIIBIsWRQMKIZl4smfbzK+X//d2E9MSVBKXKelZrBfXcKQOO7La2Lx5I0AMvJ3nsXmp49T1M0vwlSzjG4jsVfaG47ovpUnOqTAmbAduIbfJqEc9XnL2NPxyWoiYaBYXiWDCMWMWPlr0ZiAFClLx3PQIsE7cmkWbIAwkGLDeMTnlSKcIiBV2snwZ+MBJQFZzUwYA1SolJQJYFeNOVtLViRaKh1lJ3JEJ+zJ/6++7a+L6+OIgSSJ42iyu6Oopp+0mPWyYknFh48LCWdkjVev9rda2++5n137V770cTar6fUll6m9u9HF3rl5CV26PcjdMQ+EkKV+2KoxXQ6fSp2553nYy38NphCcBrRrIriY6vLDsUW/bi/lDQE8CfrqvykCfzl58SJ1SUaIsQ5BeATV3p8s0WOrhLFRh4Vmz5UfkYSM5RYM+rRWkzGYDTi91c8jVJnVQqXYZYYIe0LVmRWkKeaOVZ8iGFJXUuBhqb+WRBF6Iknidar4u8pJ9S/9JEwRIQYVDCA4e74NFsj6jaEgrEhrmzDqI8cxqYJQYkiVwKhpEmXojgKIpCwHjGvcvdWq+4CkUaBcaoa0VpLRKiiCuRe72qdZl7tMgQq7a7LSpbF6VIEqMLoHKvlM5rsbEf/zl0XWmjyjjceOW3dJ65/ee3JJWvT7h+30TM7v2uLLQe04bhZmwMOKKxK5sX06ez8NESdCOfJUQr12aiGKQNduflHhMnEQYyeq1okbndjB21sZZaG/sRMpLki5jJLBPlkKae9zO2N1LhPUWNe8dBOzDtC2zP7IareB2oYSFhxaHpcFlNRoax96lRt4kkKVNXc8RSBIlQbf9NoTbMkZtiQ3K7ZML1oe9y3hHu6sgw5M5AH9lRHU8HnmF0xIdEcSuF9N1QIA54Qe7d6Fww/vAuuKo61csrYa7lM+aFTbsSrRI5uBJFOSQRKBJInlVDkydAgYOTHBIAN6PL0d8NHdthnlZkL77P4Y5uvWHv82doS0xdb543/ueomr+ttfNpbAfTFxr7h2d1b3OI6f1XL05guqNIYKxtb69yJrWW4nbI2wY1tACXS3VO3pSLh9N2FPf5ppxVE4+abCwhCcAqc6fDDCxeb4S1yaoNps8bYMLLiQPM42eBGas1PUmVC1dK4t+5SI5eqxq8712yI5kHipXOCuDMmscQhIr1SiuLOIRKUzgpF/kbg8hbE3Bz7cTlnbf7azLtPOCOgCsWNyWiknfnlCRMKK+8Pf1i8OwQIiLRjOKMxRJUweh87nPTS6K2FUVpyvTxlxzFZ92ewy4c0hoCt4cCoFpjstJel469fYI9r+iDDXPNrVGlpKzEZx0UGubTAx/CXVqA22CEgY07JPA4LATtBOJ0I6SrBMmxPxNu5USh8BgfuKo3zso2RhyhEm8drmZR4kpkt0uzZP8IshMc4ggzhTOrUub+EzmZ3VL6mErc3vRT3RlKomIOgaCi1dsKUpZEgCl49rcvTgSe14SkczzR109CautnJ//zPwi2sNJeCEW1Cj9AddNOgy6V2yuJGyz+NWywmhbAxiWS9Yu3cOzyBylx1HIHkSR2HNBNsFwFKjZeGzWNoC1259SkGi5xpDG1bCP8kG9joK6tiMxWuyt0qW21VrO33TRIbyejB6QN/zTixChhnx8A3NKtVbzxSOXkYZ2+99Utb1HQPMpMmNSdJUUJtyDK33XYrWEBTof0o26ETE4Z2rsKTNHjqXK2pRKISZS0mfQR6I4TH/BG21NexgdTc5QhBBUiQMCYW1iOP4NzlMDMlRNPix4Y/+SDa/vsPLUJz89z4D1y4EdclyABEbqNcZUx2JqOCvg4MwG4oYtd7my0BHB+VeJKkEtU8gUDypGwGQ4OAoa2vSpgtMWEU00kGc5a1c15uzZNs2Wdzav17qQZ4KH/2s73cpZAGmowRtIKedGT1g7QaF1cjXXrUOUXPmMQgQU+UBaEaHfgTNMytuFRuuzw0FTwYT1Ww6nRb4yPYQyh/xgTVrFUx3dR5RLOc+IbIEAnHILsGMA3KpolUNRi+QawjSIxz4q9SOjRvlRuvhpoNU1DTjJcvQnlVSBnoxPSrNm8jAPzMOTuWlKPxQEhbQkEczs85p2hgVlkOT5FtAwNHxwXOYElJBJoikDypKSwZOOgI2GaaqqDy9ctED2gMTZHorOmMCOwpE9hAuTejOCK3jm8AaoqKCwI9oTe0366F4Zw3R5bgPbHLtmzTrGZMcAYhungCNABCZkjNJYOPKPXek2ATp59efBxEHDya2FnBbIp2FoJiY+KDoWbnPqCXf9Y5GhiEc7GKQ1yYRU0P+YYJzsRpCUlSj6WgOH4qFn5c8p7yaosTkTUGf7Erb5bEpeCEESseESgKRMi8CEER2FE8ztZBPIFaj1VaPDovJQLdh8BAnVK7D5Es0YJBQKdsCBvj5ngio7euXCDF0KtQA+XRmiRJ1lY33/pWYU+yT50nIkx+NnVx6fW5wySCsptWw4p8zwFlVBa8k19FF08+FshT7z6Yp5XUiRA2JJO4JmOYa8JgIlBLYoUzW2OTHBzBQvyhI0myHA5GeK18YbqqLwgK+i6QkUnjJDJeLaKMO3p6KcxDhbhFq/BaheBh8Sp5O3AjU7GaChrEF9C0NStb+PFgaeUtboSfsUpPk5Zzk85/icAoQ6ANjTTKEMniLhgETIxQ6hSAQa3u28CX1YeqsLyrJ5XQv4yZazOOpwxM8PlLPzl81oDmGNFi76WPfazGdYluM/r3ZVDTJWZVulx8h8XGzFWrEsOa76DeeGPtwx8ueIcDLygF9UAfgjmVgUN0Yh8BvIR1h6Un7EP++smHWmvEYLwOSobLyW9ZxOB7YobdNFhUBCJDaI33JQ63GxWECHeL6TYvGjyEc1yTAY3fT2Yt75rtEXA1b19kxo0IN2cdw4mURCARKBHIebcSijxZoAjo6N/xjmIRrz339Ol0ANdjC0/K8XGnchNDZwzJE0OM3T2RAWZEC9VoLTR3LjOVSkcLxgTciC5U75mn+ZkHfd+LqifKzGvb7pxBKxAHe5PzlMY+NCnsgLXNKkpcahiI3RzsjGViC6HRGuOzx0gJpiKnGJ1A5YhDSMlghEfrVdzYcEsKGoAxBluUSyKTSNZP0ZReOnYYYsSCgUECYsTCZDSCDLnRVpm8tt1yySUFWm5xLiY4u59tD4PGkFkYQQgkTxpBldVtWaXyfHzDPtrWcev9uSojSaEGOlhU42MWFzqJWSGUDaUicEhnYDpWPoUys7QghNe95YiUsz0JFpjQ6laIceQhKo/aN7mI9PjKRtAKdKBO6HlEgKs/h25mGUTSFoTMOMNAcBE2P4zWF5GNEFAcYwPtUCnZU0HLqqRxsvcw8MRfhWYBCtYnRDRHcCCcRjR38UgTIZiTps6AFNwIGeKa5a5IKrhUzMcZPLhLTNvBCzcTBzMPQjLxp3hNhgFgmYVEYFgg0NDLDItcZSZGCwJ66re9rcfCGk/r/RlLBiLUEgcVTrL0pvF0jN0te6JzU9pCwHft+fd8/euFbicmMql36nSwBRPyFXjP9XGSEKTHXON73lNYP8KAVM2D7Klm85F2iZg4saAhWIN53OFkatPgfZaY+zb/coULP24sDllxmD/Fe0zAhSUJX9FiXVUOqAfRUWJXHUQESDiQLbSJOFFcV6XsFmvZsB+jAici2yVIgkgSI5Y5OM/yFtgg3raKeJK8QatT6wB+8N0XHv3nzFm1cbYQX/0VY3d/76JF/lISgRGIQPKkEVhpoyDLunj2C5+mwJPsg2eFWk8fNOgVDBYXo2Tp2CSGgyoTA/8WH5NKJ4xeoSsiWCBmn2MGkFLsec2iQ2kPtmGJPcQaflq9FDtuWc5mdhY1qIq9Sn/2s8IsydOHwYmFzdacA+TX1fQ7es4aZB2e3GnSKBHbD45iy0eGIiQGg2f+CeOQcDBzuIZ3ECP3hi9RcCZt2Go1YJhHloI4uCLrFKqEA8UjcCwUCjCsgW7X/j3FLmXiEORJIh1/F84/e9qxR9f+8cTYOZsbjF1z5VmzZkx7z74N3+jpKLCZWCIwSAgkTxokYDPZ/iOgrz/ppNqFFxYqUndvgGv7QHYEKqR/QiWYe+GmYnoCbYrtdaSc0jsCpoKqJCluwDe5lQ02Tzr11PlIUjyawjcTZ7FfKXZHsPcoFhwM4pZbijpGGTCqvgqK4WPC6AnD1SDPy3IYUghPM+eFCckvysKiwyLGAhTL8hmBvAsIkFljV/Ebs2ZiIkk4JC6FEIrMUOqumIdEtkSWICuU1MJABQ+cyeylls9863jd6+qpZl+hahH/vHOmf/KY2kNPsAPLdY1J6f5HZ3/i2BfGjnt+9/eNb3FjXkoEhicCyZOGZ72M6lxddllBkmgrWoFQXlxfeS/ZR7jOjtA+TKYeHDRp0XOnjAgENIKmcumlhQf73/5WtA+WGV+yKElSxEcibPzAb7n9yUHUA99CxhETTAShsHpuv/067y43r0RaMs88zugm4JAbVAan4ZxnjpHJB63B4+VCvlxlNFImPIlByI0yyOYkAhd+bwfW6qd5SIkgTwiTA0kSIQYDbkGbGI14kStT7Ow6LyOd///A32Y+9DjNMvdV88J57R54dNz9981bjNf5Z2aKicAgIpBj6kEEN5OuQ8DI2Nc2zHnxtfV5CUyoUSgGqoIliarQ0TqcUAOmXJgSBijDhyQxACgX8ocUpjRHwGr/pqI14EBMPhttVNthh9rFF8+1JFUjm7itmsEOO6wgDogGrxx7UTYK08322xfe327EO9h5LDFAvwZTomGzWzGUWgqHFeFJ/npNHN4OLMrEHIbkwHX8RYOU3iVUiZ0VhZLfWK2GCeFJAiWITrEz+cnURAwPTMwpvV0kBpskeRwDkg+xVMcjc87HzgkfTEAz7URgcBBIe9Lg4JqpNiBgLsAegSxDunhy5ZXF1M3xxxcdd52Ec0Y1EL/R18e8SjV8JJ7DgQ4Lue22YojPn9cUFt2WMh8CBx5Y4wuFF9TJaacVTYGpxN9eqYzbWZV++tO5afia4Ec+Ung1S7mK+Cc+UXCTqkicScl6sMEUhYjBQDB4fxEdfMg4QR65LqFHTGYTJhQzaxzTUSIFEg3r881XTIhZjfuRS0oTicRr4iruZUtVRIrwUrJbko2/mZpgYMghBQ5STHIe0VkZiynVxqihkir50JzNwOeEd/ZRmVoisCAQyL55QaCcz4CAL4cgSXpzA19CGfi2qzE8TRSzA4GSXtuqHNqCPghFhlcJ1OlTGF0gjZvT+IiKSZM99igsTMNO2DcwWXq4KvRzfBikGtjxczrcVkkYDLtKECMPZRhhJIEXUiBQ4wheUPd02Y6duM48s/bLX853kcXSjB7TijVxpVx1VXn60gnD0mCKEiAu+Ao+FFPM2rymjjQrFoMrmGFvPs6LwBWJm53IfPXMIEMiWovNNSxtY0OSSCwBlIg2Zs0fa6XFf+J7EJMV6xvw7DzF5d1sHfOSL7/ioqJ1liqtvGpthaVm/uvZhebQI5Nuhay09MyVVhlMNDPtRGDQEEieNGjQZsLzI/CTnxTOFkGSXNFNG8BTWBajVXmSS1a36dyRKj0+0YlzR+GFTW2MdLGCL8xpdQWhGt/4xmI2adiJfb5VA5cgBsCwuCB0VOunPjXoWVXx5mh9lO1rXys0PMqw4441M2jOW5MkfMFX730QmOBYjfO7ptUYVao8Cd1oZEWDz1stzlMmOz2yMiqTjMsUww97jzlA02rBn5TDpdgNikkJbSqz5i57ams8FiaapPOTJYkFLdiPHTEcIVJDO71W3iMWTQMVj5D+oYd2eASy/8GLWd32meOmPzaZfmFVmrXKsjO/cHxt3wNzvdvcush/IwuB5Ekjq75GcG7NplVFh44eNSUNpgMs66YffV0EtTLm3nbbwum1C8Q6PhMfI0zobQdDBD2Mu1hctiCFWchEG25kDydr2cxIhQFJe2pqSUITuL+hd61F+6sKbySmlboEMcJBFnYgKzHZijhTaRhO7JFtdyrGIXsBmGvDabwmyooeCcerTLSFc5uqwGBxV28HB3S723MJF9laNsZXV+uE2SnWApqDIxiS4rrFdgodt9QeeOhitTHT/v7gjNg/ae11a+//UJKkugrJnyMGgeRJI6aqRnpGWYl8Z03PHkNho1tduSXKdQorimmOQF/vk6Z4kk68CyxJUS5ux+ecUxRq5Amzw1AJN65zz60xSFLpIS3IpgVdHMAZwLgu4UwmcRlPsKuqMLbU2e7wKlTlm9+cy9w1SlV19NHVmwbpXO68Gvb0Qob4ccfrINf2frT2TsaFIDTIjUnqN72pmDLzXRbeSxFuESg6Zz7OQKL1WALT4snELlUKUiUEJxsMOfCjSYwGA9dMcwgQSJ40BKCPzkfaRdnKa34gYUPSR5tU2W67JgPfwAeLqtNl/cPtlhueu+3Sv8549vkXa+MWr02dsfqE939iQv+SGvhd7353MeFIOdXJ5z5XrP1OaYIAXmAbya98ZT5jT53hp3qbT3V89KNFO6P/8SRGGN46XJrKWxhStDy+OVUxp8WzGw1hYFE9SLqvNHf8W4PVJ85/XjcS4J+Hp7E22fTAhwi5KCFSZtB4JhlamCRkGWJiCwtTm1uwMlBJx1YCnhUcC9sUmA1v/qrIX4lAPQLJk+oRyd+DhACzkK9y2VuPWy0OxN8ISdJND6r8/rrJ537y7jtvmjZl1qIWzI2pjV9+/AO1Kc+9/4uvGdTntkhc8evG/dTxLru85LnV4t7ReAlxseFkyXJ6heDsswsGEcIBxzaO2tyeexZOTuaoEKCjjiqcuBGjqiAOSAQaq1Ei8vhI1fBSjbmgzrljyQu2xgSL2bAYRY6QJ3NzzEiyifK1/z1Ehitzc/y1mKkkiCSx7PL8yw/4LKgqzeeMVASSJ43Umhtx+WZA8vUo63QYlkhs90I9DapM/us//nrj5GdnL7FYbSo3j5m1sQ8/v+zffvPYs8+8eqmXjR3UR/eUOCVH9VuOdN11RRTLufjGWJ6V0hwBXtjldFvzGJVQmr8kSRFsjtP+3SbUuFghWyLYbzGmfiv3zT3F39GQYSNMj00HErKJyPVVvIBsUd47E5h2WZeyPSkMV8Jdqa+pZfxEYPQgkDxp9NT10JcUKzL8bX8EPMAcT582+9EnF588e/HxtekL1wr36XG1mYvXpv36+sVWPO72w/7vaweYfr9v55n+858X/iiEkSC2P+h3annjXAQQAcyC41udmIATvgA2Mqh77vD7yYzElGZ2UdvzMuJbjpREIBFojcDQDKlb5ymvJgIdQeCff3/x9zfOmPOpkpfW2iFMM2ePHcvrZUiF4g7KmCSpl3rgPlM3T1neQMmzDEHQSVgm+fUsYGGjMjGGivFqMhPW/vzgAs7nvMchk6i55ge5JEnzUMn/iUArBJIntUInr41oBFabsPCWW85YpLAwcE4qZvhm1cZOrS2+wtIzl31NV+xZOaKrp83M8yjyRVuuy1Xh7IYYBTei+YMn+XnkkXMDq5F94QQvQGXslsmhG63plJjU85GdU04ptiHYZ59iK1UfoQ07YacekekkAonAUCOQ825DXQP5/EFDYJFFx6z16vHrTZhx2wOLokf8k2xKsMSY6VtvP+t9h8+vdwctD5lwBxDgfvyNbxTf4CgpiC3M99239vjjc1kRK45LZtYOPrjYdOvHP55LhjAnS96++tWCIdnM0S6fPJ+tdLNNNa/toFkDyR+SZG2dZZwhdiKQMWsX0+VnIKjmvYnAMEMgedIwq5DMTkcR2PBtr/jgyTPP+Oyj9942Y84Xpmbv85HF19n+VR19SCY2+AhYye8ohU3IZ9oQIPtYk5jt4tuMIflQid237C9EzDD5LI4JTvt7cl0OYnT99UUgt+4BOs9jZr7qjCQxVnlQTMBZbWeDbf7SKYlAItAtCCRP6paazHI0Q4BJ6Q3vWnv20i978vZHFpo1Y+qYJbY/cN3FlhjkVXbNcpJhnUQANWHI4Q90wgkvbXDuayQ2mcScqvtDWv6OTiFJlsUHm7E7wC9+UdiZJk4cqEnJt0KIZAkSFrN7Pp+WPKlAJCUR6BIEkid1SUVmMVogsPnblq85RoGws5iM8pc/DzPKwGeWhi9mFvAjOl/4wnw5ZNT5zGeK3ajswBiCFfnSBypTshmLvngp2W8RzYrA+ZJo+4dn8XmyIKBcYYcn8VjiL5WSCCQCXYRA8qQuqswsyihGwCyQz6/ZGodVheLmq2Oeyg7OXUuVsBxeRzHjVq13+y3ZvPv//b+5YVbDOUrHJqFxjiENkNAwJtn7WwbkJARJsn1ldX5w7oX8lwgkAiMYgeRJI7jyMuuJQImAr1uccUbxhQ9GDcu/bLtsN0uEwd6e3SnIIJtQoyAuZtlKsa8ix23f0GX4YWFDkhiBVl+9+OTsAJfF7733S1N+8TiPZr6SckoikAh0EQJpIu6iysyijFYEGDJMLjFwYALWWtlFyHIuu1hbqD4iBaf58peLtWNnnfXSrFZdSRTVlgGNwlBklX4pjEa+RuLraDy7YQQUS96E8PUeoDz6aJMEUKUBmqmaJJpBiUAiMJQIpD1pKNHPZycCHUEAT7rnnsK64ZscIZQ1IsGMMsKEvccC/o9/vCA0OMd3v1usKbNazR7SdTOIfgYrNNFWFeacgw6qBhSfIuHuzWnpT38q5sV8OGbddQdqTJrvAUP0g+3KJpzPPz/38SAClC0PkqgNUYXkY7sVgbQndWvNZrlGEQI0IzJgHglVCsExuM3U7c44AhAxX+iDtffeWyh7O0YjQxbef+tbxbdbG8VHXH/wg9qmmxbsR0xfjrWlZFNuyMi05ZbFVgJ2XfLZ4QHOuDXmZMGHPPRQUdiSJMnADTcUPAlfTkkEEoGOIpA8qaNwZmKJwFAgQO+bR2JJ8RFYc1a0J1sDa4v5pREmV11V++MfCx4TVMZfBOiaa2q//W3zgtiC0iL/448vCJB9sUXjkLRgZLfdmjzHjgDo3QKQj32s8EGrkwsvLGxmKYlAItBRBHLeraNwZmKJwFAggEtwSHr/+2s/+lFhi+Hi7GvwW29dfPSUhG3JjNbA13gNeuGmTClYnvKU4hzp43zdk9gY6dBDe7o4iOH2B//wh4ttLeFLgLvrrsVeTQtmM25TrSmJQCKwQBBInrRAYM6HJAKDgwDrEf+ccNQxs+S7Z8GTVl21tuGGRTiC8bvfFYEUq80CrP2yDXWVhwxOvvqbqiyaPvOVtBAUxGEzJOUZbmJm0HpCdrzYP4nz/A471FZbbbhlM/OTCCQCA0QgedIAAczbE4GhQYAr0h/+UPMRDuuufLGDswrr0frrF0cpuNF559UuvbRm1wDC0rHNNoXZaYBf7CjT7/zJW99a43X0058WnlbYHJJkn4Ntty343TAUflGHHDI0+Xr962tXXtnJb/oOTTHyqYnACEAgedIIqKTMYiLQiMC11xZzPvaWRCSQiptuKsjQHnvUbDddyu23FzwJ2Yi5IDNal19ee+1rC+Jh88XhKDzPbbEdX2pjCjOb6ENsH/pQ8dWRlCoC++9fO/fc4su+VUHaJk6sBuR5IpAIDByB4dlZDrxcmUIi0M0I2AzIx+kffrhgFLHdNFLx858Xa79Mt5ViCRTyNGHC3Ik2Mc1osUKxPA1TnsSGZGfML32p9qlPFfyOHzeKp5ApdQhgkFqAHaFYCInJSl+1O/DAYt1jSiKQCHQUgeRJHYUzE0sEFggClsmzJDEd8R4m6ITzRx6p3X//fDxp6tTiaumNFCesShjIsJbllqs5UlojwBmNO1TUsZiawjAlv62LkVcTgeGOQPKk4V5Dmb9EoBEBxIgbcZXuWM4mJGhTGd/33cy70aQcugmGJA7P4+rSdT5M9nS86KLajTcWetaU3HvekxacEsLhfaI6o2qHdzYzd4nAiEYg908a0dWXmR+lCFgW9upXF7zHgejwT5o+vbb22rX11psPEFtPb799saPSU0/V/v3vgjDZrnmLLV7iSZiWqRvLts4/v9jKkTnqG9+offKTxUr8lEQgEUgEEgEIpD0pm0EiMPIQ4LGz446Fs5FtBWNrobXWKr7MwRWpKsxLRx1VrID785+LL8Daifrtby8+AlsKmmW/61tvLfxbwgEcqeIhbhmdHRxTEoFEIBFIBJInZRtIBEYkAptsUmyvaG+k2BfAwnke3ObjqsLIhEVZa49UmWvzBdi6CCxM8Y2QcpWcaRwGqr/8JXlSFcg8TwQSgdGLQPKk0Vv3WfIRjQCnbLNsDrSGm0rprF0W6q67atddV3zcgteRSTrbErE51QnaFN/5CPcmV83ESS1XmNUBlT8TgURg1CKQPGnUVn0WvEsQqDMRRam4Zn/zm8Wcmt2ViO+e8T2yw6StrauCJLFL/exntaefLhbgY0tO7FWEVKUkAolAIpAIQCD9uLMZJALdhgCbkI/DIkmoTxysTWxLnJkYn6rC1GTC7r/+qzAgPf54sdkSR6Vjjik8mVISgUQgEUgEIJD2pGwGiUC3IYAM3XFHMRNX9Tp65pnaAw8U7kqYU1VYj/baq/bGNxa7A6BNG2xQbIJdt79ANX6eJwKJQCIwqhBInjSqqjsLOyoQ4GCE8VRNRyxMDuyncZIOneLfbU2cfbCduzclEUgEEoFEoEQgO8USijxJBLoEAVznTW8qWJFF/lyOHNa1MSPx5vZV+6aCIaFQSZKagpOBiUAiMJoRSJ40mms/y961CNhWe+edC4b0xBPFJpPo0e671173uq4tbxYsEUgEEoFBQiDn3QYJ2Ew2ERhKBCxkO+ywYvdtOyGZg7OobeONezQmDWVG89mJQCKQCAxvBJInDe/6ydwlAv1FwI6RW29dHCmJQCKQCCQC/UYg5936DV3emAgkAolAIpAIJAJdjkDypC6v4CxeIpAIJAKJQCKQCPQbgSGYd3vqqad+9KMf/elPf3rmmWeOOOKIzTbbLHIvfL/99itLsssuu+y+++5Lz9ns5aabbvre9743efLkTTfddLfddnuFr6WnJAKJQCKQCCQCiUAiMMgIDAFPmj59+mOPPbbUUkuhSk9YjTNPXnjhBSGTJk16teXLtdoqq6yyuH1darWHHnro0EMP3X777bfaaqtLLrnkueeeO/jgg5dffvl59+X/RCARSAQSgUQgEUgEBgWBIeBJK6+88ic/+cm77777wQcfrCsTYrThhhuWFqYxNnWp1U4//XSs6DOf+czYsWOXXHLJb3/72/fdd1/ypDro8mcikAgkAolAIpAIdByBIeBJ2M/CCy+80EILBQ0qi+Qn09Hee++96KKLTpw4kdFovfXWw42uvfZaxiS3iLn22muPGzfOhF15l5OZM2c+/fTT0+ypV7Ox3jT2qurVPE8EEoFEIBFIBBKBRKB/CAwKT5o1a9ajjz767LPPNuZprbXWWmSRRRrDhZiJO+OMM9iTnnzyye985zsnnnji5z//+dVWW+1f//rXcsstF7cstthimNOUKVM8wkkEPv744yeffPIvfPlzDme68847Izz/JgKJQCKQCCQCiUAiMBAEBoUnseicd955P//5zxtzds4556A+jeFCTLr9lw+XzxHWJhNtCJDIzEsvvvhihDMdzZ4929X4GX9XWmml448/Hqnyc+rUqW9/+9urV/M8EUgEEoFEIBFIBBKB/iEwH+HoXxKNd40fP/6DH/ygGbTGSyussEJjYGMIzoQPBSvi1v23v/0t4lgTx5LE8lQak4SbsMOlykTqWFQZnieJQCKQCCQCiUAikAj0CYFB4UmIy8vmSNOsYD8cjNiK+BLhPWboxDWhxrOb3YhxyLTa1VdfjQwJl9See+75xS9+0dYAq6666jXXXCPQUrimKWdgIpAIJAKJQCKQCCQCHURgUHhS6/xxPzrttNPwHrsAOL/44osPP/zwLbbY4rbbbrvoootYhhAp9OgDH/gAYiSpbbfd9pZbbjnppJMYmYTvsccea6yxRutH5NVEIBFIBBKBRCARSAQGjsAQ8CSGol133XWbbbYpc7/OOus4f/Ob37zmmmsyKZlTW3bZZZEhRibh4h922GE2EXBJuE0mI7y8PU8SgUQgEUgEEoFEIBEYDASGgCdhORv7dnmDmHEjDcFFAK+mNh2bmt6egYlAIpAIJAKJQCKQCPQDgfy+Wz9Ay1sSgUQgEUgEEoFEYFQgkDxpVFRzFjIRSAQSgUQgEUgE+oFA8qR+gJa3JAKJQCKQCCQCicCoQCB50qio5ixkIpAIJAKJQCKQCPQDgeRJ/QAtb0kEEoFEIBFIBBKBUYFA8qRRUc1ZyEQgEUgEEoFEIBHoBwJDsC9AP3LZ/i2+diLy888/b7Pv9u/KmIlAIpAIJAKJQCIwShDoE0PoNp70wgsvzJgx40Mf+pDNu0dJfWcxE4FEIBFIBBKBRKB9BGxbLTK2tOSSS/Z615gwwPQab6REQJL+8pe/yK0vnIyUPA9tPjWAm2+++Yorrvj0pz+9yCKLDG1muunpTzzxhK9Bn3feebl9fAerderUqVtttdW11167xBJLdDDZUZ4UA/xHPvKRT3ziE/FphFGORgeLP3369M9+9rO+tfW6170uVVKngKXlv//9748fP3633XYbyGfvkaTNNtts4YUX7jVj3WZPgtomm2zSa7EzQonArFmzfGXPducbbbSRj+uV4XkyQAR84JlR09bzyZMGiGT19ueee853jV772tf6nFE1PM8HggCe5Pvir54jA0kn761DALDLLbfcuuuuq3fVbuuu5s/+IYAnXXPNNfFhj4HwJE9vk7x2G09qv+T9q6Huu6tsKE7K8+4r5oIvUQlmebLg89CtTyxaahqMB6F2E9XOghp4Fo11jnQ28UxtgaHahTwpW09fETDdlqPzvoLWa3zvMCudv73GzAjtI2BQvtpqqyWq7SPWTkx4MnuMGzeuncgZp30EAMtQN0CbR/uPGyUxocpUvyDt9OMmTZo0SsDNYvaEAJ60+uqrr7HGGmkZ7gmifoTTOqusssqrXvWqRLUf6PV0iy5SWzXvlqj2BFE/wqG6/PLLv/KVr+Tz0Y/b85aeEADssssua96Ns7DznqJleF8R4J74ile84uUvf/mCQbXb/Lj7CnfGTwQSgUQgEUgEEoFEoCcE0rOsJ2QyPBFIBBKBRCARSARGOwLJk0Z7C8jyJwKJQCKQCCQCiUBPCCRP6gmZDE8EEoFEIBFIBBKB0Y5A8qRR1wJuuummj33sY29961snTpxYLfyUKVPOOeecHXfccZtttvnSl740c+bMuGq/xG984xvbbrvt9ttvL0JsY1q9Mc8bEdhzzz3tnBTyvve979Zbb404DzzwgM3i3/SmN4nwi1/8ovHGDGmNwOWXX77LLru8+c1vPvLIIx988MHWkfNqTwiccsop85rnxltvvfWNN94opo8ZnHDCCX7usMMOZ555pi09e7o9w0sE/vWvf51xxhk2PHz961//05/+tNy3+b777tNE9bHedF1udKc2q7vzzjttP/uWt7zlgAMO0C2U3WyZYJ5AgDICpp7zjW9849e//vXJkycLhN4RRxxRttt3vetdsae0S0899RSlBlVoX3XVVTZY6iyMuS9AZ/EcAak988wzK664ov1hzz///DK7Xu9LLrkEDdp///0t0Pj4xz8uxP689km7+OKLL7roIm+1c92odTHvfOc7yxvzpCkCf/3rX9/97ne/973vddXunUBzYvtXux5b+UJLXX/99V/84het2thiiy2appCBjQj85je/OfbYYxFNrffUU0/9/Oc/7287nx1oTGqUhzz++OO23j7ppJPgYNW6DSyc+Hn22WeHWvLXSiJv/SgHqtfie6lxHcPIr33ta08//XTEf+yxx7zjzz77rI8c/OpXvzr00EN1ofaz+Pe//61rtWDTJt1CNOCvfvWra665Zq9PGW0REB07yv7Hf/wHhgRMDAkCVBJWajv+ww8/3E/LtKPdOtdQ0frPfe5zv//977/yla/YNWDLLbfsIGjJkzoI5shIilmIXHbZZf/zP/9T5vif//wnzf2eOaL9eeGPPvpoDP0f//jHr3/967322ssVke+55x4daPKkErcWJ7akwkftDgDP2ECFAenvf//7ddddt8wyy6y11lq4lD508803XzBLW1tkdaRc+vGPf8wUZzjuUwOWBB900EEMIdttt91Iyf/wyacmZwuAlVZayUm0TwrpBz/4wYknnqhzMCKi4215zLBEuw+fbA/DnGA5H/7wh2Ws2p0+8sgj7EbUNsOnFstuZLR58MEHO6HpmecnTJiALX30ox+96667nOTOVXU1q4fce++9BZrNqF7SXI0ttVvbgpT9KqjBy7a0wQYbrL/++szMf/jDH3yQpIMbLOW8W7UWRu+55sjO5I3V+KDA9m7ESZHrLvF6O1UENJS6Vz3Y/egFq42S27WPUqdmvO0XXHABGN109913e41jixpWkNe85jUPPfSQ8Wgb6WWUYlbo/vvvN7sROyfpLqnwnHrrX8ugQmiXnXbayYTR6aefTnlrnKwdXnwJaqK2/DFAp+/7l/5ovostRENl0oAhHEDtrf/Tn/7k/I9//CNehSQ5d9WWdbR7ejJAo00BptEm5xBDd/MhdJYbsaKVV14ZSXJuaOrrO/AvbXttptw6WtqTWuMzIq9qIn/7299CN1cLYPbHGxtMqBrunBJiPS4vUfOMnNKhjbz2odpFQ/M1TR937CBVr8vJSPnJMsTZqNG9wMaSXlqmOOQSnqxxPtkIxl133dWQHT0K65ER5NJLL40k0Ua61JFS6iHMJ6CIFhh5YKLTCJH4IczSyH00Bs8OZ4ad5jbXpn2awtBcy335Wew0VIalkVvGocq5YaSeU59ZvtdQZZiPHrWcJkb3NWB9gvChyurIei7EDDtZ7+Bm9oOXCD3F/Mk5yUmURRx+DtotjdbB0iVP6iCYwyUpE7qXXnopR8K6DDFFGsqUZKh6ldrWLZZaP4wcXnXNTnjpFqf9aYW5DT/oDMdZ2lHGKozOOXgZ0xjxRDhuqiJuu+02mgl03t7oFnWmziGZYNYB2NNPmpuUmhuAmqWQnuJneAsEdAVxde2113744YeNyLl9eP2jTboUNuNsnC0w7OmSDlPPCczSUATVGGpGDxA36gc0YCE9pZPhdQgAtvQ6YpYzZandmtlEm8p+GKqQ1247O5WZPKmuLrrhJw9N9oyS3JRFwpDK0UwZGCc+QmT0g5hrZFoYvW6IqS0yxWuF5uAimpk45uJUTtCgVyibxrEghKuvKKIJLh0lxcPMfvXVV0e9eLGZowzie6qRugrKn9qh78BogYE5wmSymOkukRkIAnQPpaJNapz6h9tvv93Mpp9eeeHlMH0gjxht93r9OTCwfXL6NEwCJtszMzOo11tvPV5frE3MolovkCn+JKP9aCH6Vc1Vv6o3gC2ozR0boFoo59xJZ/vV9E/qRx0N91u8eN5Djq51YqJH84oX2CtKc3uBtSrjHhqIu8wvf/nL//3f/8XTTzvtNM7abtfghJsS5pZkKQF3uXDoHu4QDH7+qG2dYB3Cfho4GqBz0PYXtiAFHXJp4Mij04ttGs4lS7f+/Oc/00nZS7ZZV5ou75lrr732hhtu0G4tGwadhW9t3p7RqghYbHXvvfc++uij5oVByreDoP6WX3FU4kzDIM3PA3mq3pXnjQgYWD755JMaJBt8nJhKg5seGIb8H/wFtQVx7jWy0tn+8Ic/ZGO+8MILKfUNN9wwe4BGVOkmDh5Q9ZfoMGELYWBySTSJecUVV9xyyy2+82h4rxNgFuVmB1XtWb+60UYblTPIjYn3IyS/79YP0Eb2LUiPDT8MHHWIjJaYkAXqBjpeaZum8CvE0I0jfSBZoF7gjjvuOOuss0QWrjM96qijVl111ZENwSDnHrYg1Ql6DjplyLj77rtbowHMK6+80vusF9A50vrvf//79aeDnJ3uSZ5185vf/CaKaWhuvGiK821ve1vVetc9RR3kkuy7777A9BDtcNNNN7VRDXc6q1kt0RIOXmN0+y/4O8gZGfHJG3DqTtmJDSONNlmSuMYfeOCB2Od3vvMdOl4J7fi1zz770OisyLZfOffcc3UFkOeM7FLpxjTisehcAfgdWgfDc479WKpWB3vTqR7t1lBT+zTs1K8atOtXRYD2ySefzHRnNGVrQO253DKgI5lKntQRGEdSIoY75tc0qci019XkRUyou0Ro8fDr1BzF8UrrOkPrm56j1yN8JJV5weZVb6j3DO8ExmGI6QoDNC+5SyKAnRdtZwc9C7aUQ/M07VNr1CbDngfeocnHCH+qhWwaoZGPdsjMrB1SMH5qnFSUtipEu00O2ms960iZOqqLZuBp8Ale3azZYRgyM2uu0QPEfLHOwXQ8g7SON7vTRpC94GYng8rHVSoJJYrZDyHQ0z4FBnri88qFObT1qwZRnUU1eVJjHWVIIpAIJAKJQCKQCCQCBQLpn5TtIBFIBBKBRCARSAQSgeYIJE9qjkuGJgKJQCKQCCQCiUAikDwp20AikAgkAolAIpAIJALNEUie1ByXDE0EEoFEIBFIBBKBRCB5UraBRCARSAQSgUQgEUgEmiOQPKk5LhmaCCQCiUAikAgkAolA8qRsA4lAIpAIJAKJQCKQCDRHIL/v1hyXDE0EEoFhgsBll1123HHH1WXG3n1HHHGEDxRccMEFvhJjd0T7y73hDW849thjbYJsZ3mBItjG142uTpw4UeAmm2zisycf+chHqqnZPP0d73hHNcRult/97nevu+66+++//4ADDrAzdfVqnicCicCoQiB50qiq7ixsIjDyEPDdMR8xkG9fLPnCF77gG0+24baRtBCfjPDN5kMOOWS11VbDaXxNT6DNeX0U0/d5fKEMkbI5shAfLvR9KFdtnSwEnfKlDj9JfPogzuOvjX19BtEHEHwmRVLVS3meCCQCow2B5EmjrcazvInACEPAhyCITPvOpe86+diTrz346YPNQnw7b4cddvCZgo033njXXXeNsvl8gW8Mu+qTrmFSqpbZ5w58qXSdddapBlbPfRzqmGOOEcJYVQ3P80QgERiFCCRPGoWVnkVOBLoBAXTHN55+97vfrb/++qbh/PT9Qd8pi7K96U1vMhN3+eWX+yS7S9UCMyn56ngZsuaaa/pcVPkzTxKBRCARqCKQPKmKRp4nAonAiEFg1VVX9WFwXkdsPxgSuuPr61tssUUUADfabrvt+B7deuutm2++ebVUjExf+tKXfCwzAk8//XSMCt/y0/dK2auWW265avw8TwQSgdGMQPKk0Vz7WfZEYAQjYHKN4zZz0b333nvPPffcdNNN++yzz5VXXmnWLErFOQlD+slPfrLeeutVy2lK7jOf+Qw+FIE+2z5lypTvf//7fq688som8pInVeHK80RglCOQPGmUN4AsfiIwshFgN8KHCD+ks88+++abb95pp52iSGbTWJiserv++uurheTkhBvx5i4Dl1pqqRNOOMFP03aLL754GZ4niUAikAgkT8o2kAgkAiMSASvR2JB4Jk2YMGH27NnO/a3zzmZJsub/3HPPdaks5IsvvsiAZPuACOEVjjmZeisjOHnssceseiOiPfTQQybp6iJUI+d5IpAIdDECyZO6uHKzaIlANyOA+phxO//88y34t02An3YN2HTTTWP9f5ScfWiPPfaIbQVKLGyhdOihh5bO3Wbr9tprr/JqnDAvcWP685///PDDD9t6gF3q4IMProuTPxOBRGA0IDCmOswaDQXOMiYCicAIRcB2kU888YQl/bGoza5IVq7ZE3LGjBlKZFMlfkVmzYQ/9dRTdgpgaoqS/uMf/7Af0uqrr85NWyL//Oc/qwi4yzRcNcQ5G9Lzzz8fgZKyMUF1nq4ucv5MBBKBLkYgeVIXV24WLRFIBBKBRCARSAQGhEB+321A8OXNiUAikAgkAolAItDFCCRP6uLKzaIlAolAIpAIJAKJwIAQSJ40IPjy5kQgEUgEEoFEIBHoYgSSJ3Vx5WbREoFEIBFIBBKBRGBACCRPGhB8eXMikAgkAolAIpAIdDECyZO6uHKzaIlAIpAIJAKJQCIwIASSJw0Ivrw5EUgEEoFEIBFIBLoYgeRJXVy5WbREIBFIBBKBRCARGBACyZMGBF/enAgkAolAIpAIJAJdjEDypC6u3CxaIpAIJAKJQCKQCAwIgeRJA4Ivb04EEoFEIBFIBBKBLkbg/wMUb7jiw+/vmwAAAABJRU5ErkJggg=="
        }
      },
      "cell_type": "markdown",
      "metadata": {
        "id": "394a7829ce06"
      },
      "source": [
        "![image.png](attachment:830d2fb9-2eb8-481f-b3ff-47129a741321.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EWDzewJyiCHc"
      },
      "source": [
        "We can visually see many blue clusters (30-50), depicting different normal types of log sequences. The red dots are the abnormal log sequences that ultimately should be identified as outliers. While many of the red dots are clustered together, some are scattered and at the periphery of blue clusters."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BVy4K8xnh_Ew"
      },
      "source": [
        "## Detect outliers with BigQuery vector search"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dyCvM3ssh_E0"
      },
      "source": [
        "Let's implement a simple outlier detection logic leveraging BQ built-in optimized vector search using simple SQL."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YTp-jko3ln7B"
      },
      "source": [
        "### Split dataset into training and test"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7CBZnNDOmkHH"
      },
      "source": [
        "First, let's split the dataset into training and test datasets in BigQuery.\n",
        "\n",
        "We use a session cutoff to split the first 50,000 sessions (ordered chronologically) to use for 'training' and then a subsequent 20,000 sessions for testing. Since this is semi-supervised learning on the negative class only (normal logs), we use only the normal logs sequences in the first 50,000 sessions.\n",
        "\n",
        "In the context of BigQuery vector search, the vector index uses a k-means algorithm to cluster these records as they are ingested. That process is managed by BigQuery during and after index creation. We still writing the training and test dataset in separate table to facilitate testing of other ML models in subsequent section."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IWM6FBBDYadc"
      },
      "outputs": [],
      "source": [
        "session_id_cutoff = 50000  # Split train and eval datasets\n",
        "num_sessions = 20000  # Number of sessions to use for testing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gVH6LbLBpRuw"
      },
      "source": [
        "Retrieve and write test and training datasets in separate tables"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1D4PINTvTe26"
      },
      "outputs": [],
      "source": [
        "# Train dataset\n",
        "sql = f\"\"\"\n",
        "SELECT * FROM `vs_logs_demo.hdfs_full_embeddings`\n",
        "WHERE\n",
        "  abnormal=FALSE\n",
        "  AND sessionId <= {session_id_cutoff}\n",
        "\"\"\"\n",
        "y_train = bpd.read_gbq(sql, index_col=\"sessionId\")\n",
        "\n",
        "# Persist the train dataset in a separate table to be used later for other models e.g. scikit-learn algor\n",
        "y_train.to_gbq(\"vs_logs_demo.hdfs_full_embeddings_train\", if_exists=\"replace\")\n",
        "\n",
        "# Test dataset\n",
        "sql = f\"\"\"\n",
        "SELECT\n",
        "  sessionId, blockId, embeddings,\n",
        "  CAST(abnormal AS INT64) AS label\n",
        "FROM `vs_logs_demo.hdfs_full_embeddings`\n",
        "WHERE sessionId > {session_id_cutoff}\n",
        "ORDER BY sessionId ASC\n",
        "LIMIT {num_sessions}\n",
        "\"\"\"\n",
        "y_test = bpd.read_gbq(sql, index_col=\"sessionId\")\n",
        "\n",
        "# Persist the test dataset in a separate table to be used several times with different vector search configurations\n",
        "y_test.to_gbq(\"vs_logs_demo.hdfs_full_embeddings_test\", if_exists=\"replace\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hRS72B88h_E3"
      },
      "source": [
        "### Predict anomalies with vector search"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aY4PKVQy2MDJ"
      },
      "source": [
        "The premise behind outlier detection is to identify log sequences that do not belong to any known 'normal' blue clusters. Given a log sequence test vector, if there is a sufficient number of 'normal' neighboring base vectors within a certain distance, then that log sequence is considered 'normal'. Otherwise, it is considered an outlier or an anomaly."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d2iVXlbdWFSm"
      },
      "source": [
        " To get the best performance in terms of outlier detection, the following procedure uses a brute force search to maximize recall of top-k closest 'normal' neighbors for every query vector."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TEkbws0mh_E3"
      },
      "outputs": [],
      "source": [
        "def predict_anomalies(n_neighbors, distance_threshold):\n",
        "    sql = f\"\"\"\n",
        "    SELECT\n",
        "      query.blockId AS blockId,\n",
        "      ANY_VALUE(query.sessionId) AS sessionId,\n",
        "      COUNTIF(distance < {distance_threshold}) AS similar_normal_instances,\n",
        "      IF(COUNTIF(distance < {distance_threshold}) >= {n_neighbors}, 0, 1) as predicted\n",
        "    FROM VECTOR_SEARCH(\n",
        "      (SELECT * FROM `vs_logs_demo.hdfs_full_embeddings` WHERE abnormal=FALSE AND sessionId <= {session_id_cutoff}), # TreeAH index\n",
        "      'embeddings',\n",
        "      TABLE `vs_logs_demo.hdfs_full_embeddings_test`,\n",
        "      --(SELECT blockId, sessionId, embeddings FROM `vs_logs_demo.hdfs_full_embeddings` WHERE sessionId > {session_id_cutoff} ORDER BY sessionId ASC LIMIT {num_sessions}),\n",
        "      top_k => {n_neighbors}, # Increase top_k value to 3x if using TreeAH with post-filtering\n",
        "      distance_type => 'COSINE',\n",
        "      options => '{{\"use_brute_force\":true}}'\n",
        "      --options => '{{\"fraction_lists_to_search\":0.1}}'\n",
        "    )\n",
        "    GROUP BY\n",
        "      query.blockId\n",
        "    \"\"\"\n",
        "    return bpd.read_gbq(sql, index_col=\"sessionId\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cZsxyyQnYq6s"
      },
      "source": [
        "**Note**: While the above procedure runs a brute force job for best outlier detection performance, you can still experiment with using the vector index by setting `fraction_lists_to_search` to optimize speed and cost while trading off some recall. For example, you could set it to 0.1 to only search only 10% of the clusters, those with the closest centroids, thereby reducing total latency and slot usage. Since we're using a TreeAH index, the prefilters on the base table are treated as post-filters and may reduce the top-k result set. In that case, make sure to multiply top_k value by say 3x to have a sufficient result set post-filtering."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T91-nLT25rpH"
      },
      "source": [
        "Let's run the batch prediction: this will compare 20,000 test records against 47,770 'normal' base records, essentially doing 955 Millions distance calculations of 768-dimentional vectors. The following completes in **only 1 minute** given the small base table and sufficient parallel shards."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jv4n_N51h_E3"
      },
      "outputs": [],
      "source": [
        "n_neighbors = 10  # Number of neighbors to compare with\n",
        "dist_threshold = 0.004\n",
        "# Neighbor within this distance is a match\n",
        "\n",
        "# Batch predict on the test set\n",
        "y_pred = predict_anomalies(n_neighbors, dist_threshold)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "welzwxwC6gW6"
      },
      "source": [
        "Let's see how much data was processed and the total time it took to predict anomalies across 20,000 test records. Replace `insert-job-id` with the BigQuery job ID returned in the last cell output."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7ZaKLkSHh_E4"
      },
      "outputs": [],
      "source": [
        "vs_job_id = \"[insert-job-id]\"\n",
        "\n",
        "client = bigquery.Client()\n",
        "job = client.get_job(vs_job_id)\n",
        "\n",
        "print(f\"Job ID: {vs_job_id}\")\n",
        "print(\n",
        "    f\"Total Bytes Processed: {round(job.total_bytes_processed / (1024 * 1024) , 2) } MB\"\n",
        ")\n",
        "print(f\"Slot-hours Used: {round(job.slot_millis / (1000 * 60 * 60), 2)}\")\n",
        "\n",
        "start_time = job.started\n",
        "end_time = job.ended\n",
        "execution_time = (end_time - start_time).total_seconds()\n",
        "print(f\"Query Execution Time: {execution_time:.2f} seconds\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2fhhcrCF6-gC"
      },
      "source": [
        "Make sure the predicted result set `y_pred` is of the same size as the test dataset `y_test`, that is 20,000 records:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_J7BqRzJh_E4"
      },
      "outputs": [],
      "source": [
        "print(y_test.shape)\n",
        "print(y_pred.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qmZ1waubh_E5"
      },
      "source": [
        "### Evaluate performance"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kUxPZ0x-ASfL"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import (\n",
        "    ConfusionMatrixDisplay,\n",
        "    accuracy_score,\n",
        "    classification_report,\n",
        "    confusion_matrix,\n",
        "    f1_score,\n",
        "    recall_score,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AlrYy0Mch_E5"
      },
      "outputs": [],
      "source": [
        "cnf_matrix = confusion_matrix(\n",
        "    y_test[\"label\"].to_pandas(), y_pred[\"predicted\"].to_pandas()\n",
        ")\n",
        "print(cnf_matrix)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EcXfacuEAkf9"
      },
      "outputs": [],
      "source": [
        "disp = ConfusionMatrixDisplay(\n",
        "    confusion_matrix=cnf_matrix, display_labels=[\"normal\", \"abnormal\"]\n",
        ")\n",
        "disp.plot()\n",
        "plt.show()"
      ]
    },
    {
      "attachments": {
        "3823d730-4998-48f5-86c8-874384d16f68.png": {
          "image/png": "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"
        }
      },
      "cell_type": "markdown",
      "metadata": {
        "id": "1b7d5522cd39"
      },
      "source": [
        "You should see the following confusion matrix rendered:\n",
        "\n",
        "![image.png](attachment:3823d730-4998-48f5-86c8-874384d16f68.png)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "88Nw7619Akf-"
      },
      "outputs": [],
      "source": [
        "target_names = [\"normal\", \"abnormal\"]\n",
        "report = classification_report(\n",
        "    y_test[\"label\"].to_pandas(),\n",
        "    y_pred[\"predicted\"].to_pandas(),\n",
        "    target_names=target_names,\n",
        ")\n",
        "print(\"Classification Report:\\n\", report)"
      ]
    },
    {
      "attachments": {
        "9f900d6f-09c7-479a-b93d-c441e1a97dec.png": {
          "image/png": "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"
        }
      },
      "cell_type": "markdown",
      "metadata": {
        "id": "19c10f1027b6"
      },
      "source": [
        "You should observe the following classification metrics:\n",
        "\n",
        "![image.png](attachment:9f900d6f-09c7-479a-b93d-c441e1a97dec.png)    "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7d67bcafbbf8"
      },
      "source": [
        "In particular, results for 'abnormal' show 92% recall and 90% precision:\n",
        "\n",
        "|  | precision |   recall | f1-score | support |\n",
        "|---|---|---|---|---|\n",
        "| normal    |   1.00   |   1.00  |    1.00   |  19169 |\n",
        "| abnormal    |   0.90    |  0.92  |    0.91   |    831 |"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GZJ6u6Bih_E5"
      },
      "source": [
        "#### Grid search to tune hyper-parameters\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1aw_kVg0h_E6"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import f1_score, precision_score, recall_score\n",
        "\n",
        "# Define the parameter grid for BQ vector search (adjust values as needed)\n",
        "param_grid = {\n",
        "    \"n_neighbors\": [6, 10, 12, 15],  # Number of neighbors to consider\n",
        "    \"dist_threshold\": [0.004, 0.006, 0.008, 0.01],  # Distance threshold for a match\n",
        "}\n",
        "\n",
        "# Initialize variables to track the best score and parameters\n",
        "best_f1 = 0\n",
        "best_recall = 0\n",
        "best_params = {}\n",
        "\n",
        "y_test_pd = y_test[\"label\"].to_pandas()\n",
        "\n",
        "# Manually iterate through parameter combinations\n",
        "for n_neighbors in param_grid[\"n_neighbors\"]:\n",
        "    for dist_threshold in param_grid[\"dist_threshold\"]:\n",
        "        # No need to train any model - simply do a vector search\n",
        "\n",
        "        # Make predictions on the test set\n",
        "        y_pred_df = predict_anomalies(n_neighbors, dist_threshold)\n",
        "        y_pred_pd = y_pred_df[\"predicted\"].to_pandas()\n",
        "\n",
        "        # Calculate F1-score\n",
        "        f1 = f1_score(y_test_pd, y_pred_pd)\n",
        "        # Calculate Recall\n",
        "        recall = recall_score(y_test_pd, y_pred_pd)\n",
        "        # Calculate Precision\n",
        "        precision = precision_score(y_test_pd, y_pred_pd)\n",
        "\n",
        "        # Update f1 score and parameters if current combination is better\n",
        "        if f1 > best_f1:\n",
        "            best_f1 = f1\n",
        "            best_params = {\"n_neighbors\": n_neighbors, \"dist_threshold\": dist_threshold}\n",
        "\n",
        "        print(\n",
        "            f\"Parameters: n_neighbors={n_neighbors}, dist_threshold={dist_threshold}, Recall: {recall}, Precision: {precision}, F1-Score: {f1}\"\n",
        "        )\n",
        "\n",
        "# Print the best parameters and score\n",
        "print(\"Best Parameters:\", best_params)\n",
        "print(\"Best F1-Score:\", best_f1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X4VepQhgh_Er"
      },
      "source": [
        "## Detect outliers with scikit-learn algorithms"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KqZSOjAE8hL3"
      },
      "source": [
        "As a baseline comparison, we'll evaluate popular [outlier detection algorithms](https://scikit-learn.org/stable/modules/outlier_detection.html#overview-of-outlier-detection-methods) implemented in scikit-learn, using the same logs embeddings that were generated and stored in BigQuery. [One-Class SVM](https://scikit-learn.org/stable/modules/outlier_detection.html#novelty-detection) and [Local Outlier Factor](https://scikit-learn.org/stable/modules/outlier_detection.html#novelty-detection-with-local-outlier-factor) are both semi-supervised learning methods suitable for novelty detection, and are available in the `svm` and `neighbors` modules respectively:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iA2joe7Uh_Er"
      },
      "outputs": [],
      "source": [
        "from sklearn import neighbors, svm"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "25svuP1jovxf"
      },
      "source": [
        "### Use binary classifiers (semi-supervised on negative class only)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oAUh7rFPovxg"
      },
      "source": [
        "We will use the same training and test datasets persisted in BigQuery tables"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "g2djOrYJxqsa"
      },
      "outputs": [],
      "source": [
        "# read a BigQuery table to a BigQuery DataFrame\n",
        "df_train = bpd.read_gbq(\n",
        "    \"vs_logs_demo.hdfs_full_embeddings_train\", index_col=\"sessionId\"\n",
        ")\n",
        "\n",
        "# X_train = list(df_train['embeddings'].to_list())\n",
        "X_train = df_train[[\"embeddings\"]].to_list()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gtwOE_j5sdt_"
      },
      "outputs": [],
      "source": [
        "df_test = bpd.read_gbq(\"vs_logs_demo.hdfs_full_embeddings_test\", index_col=\"sessionId\")\n",
        "\n",
        "X_test = df_test[\"embeddings\"].to_list()\n",
        "y_test = df_test[\"label\"].to_list()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YxLPlWO5ovxg"
      },
      "outputs": [],
      "source": [
        "print(np.array(X_train).shape)\n",
        "print(np.array(X_test).shape)\n",
        "print(np.array(y_test).shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O2GoQSEQovxg"
      },
      "source": [
        "#### Use Novelty Detection with One-Class SVM"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3FzknanWovxg"
      },
      "outputs": [],
      "source": [
        "# Train a single-class classifier (e.g., One Class SVM)\n",
        "clf = svm.OneClassSVM(\n",
        "    nu=0.02, kernel=\"rbf\", gamma=\"auto\"\n",
        ")  # Adjust parameters as needed (highest recall, then f1-score)\n",
        "# clf = svm.OneClassSVM(nu=0.01, kernel=\"poly\", gamma='scale')  # Adjust parameters as needed (highest f1-score)\n",
        "clf.fit(X_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BJJW8JJtovxg"
      },
      "outputs": [],
      "source": [
        "# Make predictions on the test set (consider outliers as the positive class)\n",
        "y_pred = clf.predict(X_test)\n",
        "y_pred = [1 if y == -1 else 0 for y in y_pred]  # Convert -1 (outlier) to 1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Qmp0u021ovxh"
      },
      "outputs": [],
      "source": [
        "cnf_matrix = confusion_matrix(y_test, y_pred)\n",
        "cnf_matrix"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gUEOX79Sovxh"
      },
      "outputs": [],
      "source": [
        "disp = ConfusionMatrixDisplay(\n",
        "    confusion_matrix=cnf_matrix, display_labels=[\"normal\", \"abnormal\"]\n",
        ")\n",
        "disp.plot()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "V24LufrGovxh"
      },
      "outputs": [],
      "source": [
        "# Evaluate the classifier\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "target_names = [\"normal\", \"abnormal\"]\n",
        "report = classification_report(y_test, y_pred, target_names=target_names)\n",
        "\n",
        "print(f\"Accuracy: {accuracy}\")\n",
        "print(\"Classification Report:\\n\", report)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aNNvclFYovxh"
      },
      "source": [
        "Let's do some naive hyperparameter tuning"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "y4UmwsRgovxh"
      },
      "outputs": [],
      "source": [
        "# Define the parameter grid for hyperparameter tuning\n",
        "param_grid = {\n",
        "    \"nu\": [0.01, 0.02],  # Regularization parameter (controls the fraction of outliers)\n",
        "    \"kernel\": [\"linear\", \"rbf\", \"poly\"],  # Kernel type\n",
        "    \"gamma\": [\"scale\", \"auto\"],  # Kernel coefficient for 'rbf', 'poly', and 'sigmoid'\n",
        "    # 'gamma': ['scale', 'auto', 0.1, 1, 10]  # Kernel coefficient for 'rbf', 'poly', and 'sigmoid'\n",
        "}\n",
        "\n",
        "# Initialize variables to track the best score and parameters\n",
        "best_f1_score = 0\n",
        "best_params = {}\n",
        "\n",
        "# Manually iterate through parameter combinations\n",
        "for nu in param_grid[\"nu\"]:\n",
        "    for kernel in param_grid[\"kernel\"]:\n",
        "        for gamma in param_grid[\"gamma\"]:\n",
        "            # Create and train the OneClassSVM classifier\n",
        "            clf = svm.OneClassSVM(nu=nu, kernel=kernel, gamma=gamma)\n",
        "            clf.fit(X_train)\n",
        "\n",
        "            # Make predictions on the test set\n",
        "            y_pred = clf.predict(X_test)\n",
        "            y_pred = [1 if y == -1 else 0 for y in y_pred]\n",
        "\n",
        "            # Calculate F1-score\n",
        "            f1 = f1_score(y_test, y_pred)\n",
        "            # Calculate Recall\n",
        "            recall = recall_score(y_test, y_pred)\n",
        "            # Calculate Precision\n",
        "            precision = precision_score(y_test_pd, y_pred_pd)\n",
        "\n",
        "            # Update best score and parameters if current combination is better\n",
        "            if f1 > best_f1_score:\n",
        "                best_f1_score = f1\n",
        "                best_params = {\"nu\": nu, \"kernel\": kernel, \"gamma\": gamma}\n",
        "\n",
        "            print(\n",
        "                f\"Parameters: nu={nu}, kernel='{kernel}', gamma='{gamma}', Recall: {recall}, Precision: {precision}, F1-Score: {f1}\"\n",
        "            )\n",
        "\n",
        "# Print the best parameters and score\n",
        "print(\"Best Parameters:\", best_params)\n",
        "print(\"Best F1-Score:\", best_f1_score)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ar0_xsdOovxh"
      },
      "source": [
        "#### Use Novelty Detection with Local Outlier Factor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cXRKK_Z1ovxh"
      },
      "outputs": [],
      "source": [
        "# lof = neighbors.LocalOutlierFactor(novelty=True, contamination=0.3, n_neighbors=12)\n",
        "lof = neighbors.LocalOutlierFactor(novelty=True, contamination=\"auto\", n_neighbors=5)\n",
        "lof.fit(X_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4MytWnctovxh"
      },
      "outputs": [],
      "source": [
        "# Make predictions on the test set (consider outliers as the positive class)\n",
        "y_pred = lof.predict(X_test)\n",
        "y_pred = [1 if y == -1 else 0 for y in y_pred]  # Convert -1 (outlier) to 1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "p0NWdGtcovxi"
      },
      "outputs": [],
      "source": [
        "cnf_matrix = confusion_matrix(y_test, y_pred)\n",
        "cnf_matrix"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Asx1Yemyovxi"
      },
      "outputs": [],
      "source": [
        "disp = ConfusionMatrixDisplay(\n",
        "    confusion_matrix=cnf_matrix, display_labels=[\"normal\", \"abnormal\"]\n",
        ")\n",
        "disp.plot()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6Ze54m7dovxi"
      },
      "outputs": [],
      "source": [
        "# Evaluate the novel detection\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "target_names = [\"normal\", \"abnormal\"]\n",
        "report = classification_report(y_test, y_pred, target_names=target_names)\n",
        "\n",
        "print(f\"Accuracy: {accuracy}\")\n",
        "print(\"Classification Report:\\n\", report)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "849b1e62a910"
      },
      "source": [
        "Let's do some naive hyperparameter tuning"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dC0C1Fv7ovxi"
      },
      "outputs": [],
      "source": [
        "# Define the parameter grid for LocalOutlierFactor (adjust values as needed)\n",
        "param_grid = {\n",
        "    \"n_neighbors\": [5, 10, 15, 20],  # Number of neighbors to consider\n",
        "    \"contamination\": [\n",
        "        0.01,\n",
        "        0.02,\n",
        "        0.1,\n",
        "        0.2,\n",
        "        \"auto\",\n",
        "    ],  # Estimated proportion of outliers in the data set\n",
        "    \"novelty\": [True],  # Must be True for anomaly detection\n",
        "}\n",
        "\n",
        "# Initialize variables to track the best score and parameters\n",
        "best_f1_score = 0\n",
        "best_recall = 0\n",
        "best_params = {}\n",
        "\n",
        "# Manually iterate through parameter combinations\n",
        "for n_neighbors in param_grid[\"n_neighbors\"]:\n",
        "    for contamination in param_grid[\"contamination\"]:\n",
        "        # Create and train the OneClassSVM classifier\n",
        "        clf = neighbors.LocalOutlierFactor(\n",
        "            novelty=True, contamination=contamination, n_neighbors=n_neighbors\n",
        "        )\n",
        "        clf.fit(X_train)\n",
        "\n",
        "        # Make predictions on the test set\n",
        "        y_pred = clf.predict(X_test)\n",
        "        y_pred = [1 if y == -1 else 0 for y in y_pred]\n",
        "\n",
        "        # Calculate F1-score\n",
        "        f1 = f1_score(y_test, y_pred)\n",
        "        # Calculate Recall\n",
        "        recall = recall_score(y_test, y_pred)\n",
        "        # Calculate Precision\n",
        "        precision = precision_score(y_test, y_pred)\n",
        "\n",
        "        # Update best score and parameters if current combination is better\n",
        "        if f1 > best_f1_score:\n",
        "            best_f1_score = f1\n",
        "            best_params = {\"n_neighbors\": n_neighbors, \"contamination\": contamination}\n",
        "\n",
        "        print(\n",
        "            f\"Parameters: n_neighbors={n_neighbors}, contamination={contamination}, Recall: {recall}, Precision: {precision}, F1-Score: {f1}\"\n",
        "        )\n",
        "\n",
        "# Print the best parameters and score\n",
        "print(\"Best Parameters:\", best_params)\n",
        "print(\"Best F1-Score:\", best_recall)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ihfuDUFXm-lO"
      },
      "source": [
        "## Summary"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SlEDYC1PglVH"
      },
      "source": [
        "Here's the summary of the HDFS logs outlier detection you did in this notebook:\n",
        "\n",
        "| Outlier Detection Method | Recall | Precision | F1-score |\n",
        "|---|---|---|---|\n",
        "| **Embeddings + BQ Vector Search** | **0.92** | **0.90** | **0.91** |\n",
        "| Embeddings + LocalOutlierFactor | 0.85 | 0.73 | 0.78 |\n",
        "| Embeddings + OneClassSVM | 0.81 | 0.66 | 0.73 |\n",
        "\n",
        "These metrics show how **BigQuery vector search** can perform similar, or even better than common novelty or outlier detection algorithms. This demonstrates the robustness of its underlying native k-means clustering. These results stem from a combination of factors:\n",
        "\n",
        "- First, Gemini 2.0 effectively summarizes logs sequence including any abnormal activity.\n",
        "- Second, the vector embeddings generated by the text embedding model are well suited for clustering similar normal log sequences together, capturing nuances and representing most abherrations into outliers.\n",
        "- Finally, vector search offers speed and flexibility in fine-tuning distance and neighbor count parameters to optimize the balance between precision and recall.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vK-9z_T_gSxa"
      },
      "source": [
        "Now, let's compare with other leading methods such as DeepLog, N-gram, IM and PCA which were evaluated on HDFS logs dataset by [DeepLog: Anomaly Detection and Diagnosis from System Logs through Deep Learning](https://dl.acm.org/doi/10.1145/3133956.3134015) paper:\n",
        "\n",
        "| Outlier Detection Method | Recall | Precision | F1-score |\n",
        "|---|---|---|---|\n",
        "| DeepLog (RNN using LSTM) | 0.96 | 0.95 | 0.96 |\n",
        "| N-gram | 0.96 | 0.92 | 0.94 |\n",
        "| **Embeddings* + BQ Vector Search** | **0.92** | **0.90** | **0.91** |\n",
        "| Invariants Mining (offline) | 0.95 | 0.88 | 0.91 |\n",
        "| PCA (offline) | 0.67 | 0.98 | 0.79 |\n",
        "\n",
        "\\* Given notebook constraints, this method was evaluated using a test subset of 20k log sessions (equivalent to 400,000 log entries) as opposed to the entire HDFS dataset. This method was also evaluated without incremental updates to the index/model.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0ojJDnje0Hv_"
      },
      "source": [
        "Some observations:\n",
        "\n",
        "* Similar to N-gram and DeepLog, vector search-based outlier detection only requires a very small training set, about 1-10% of normal logs.\n",
        "\n",
        "* Unlike N-gram and DeepLog, vector search-based outlier detection **requires neither training nor serving a custom model**. Vector search results can improve with newly ingested data (e.g. normal log entries that were false positives) without necessarily requiring model retraining, or re-indexing in the case of vector index. However, re-indexing is recommended when a large dataset is added after initial index creation, to help handle data skew.\n",
        "\n",
        "* Unlike IM and PCA offline techniques, vector search-based outlier detection can be applied in a continuous fashion with streaming logs."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IL6KstPsuuBZ"
      },
      "source": [
        "### Next steps\n",
        "\n",
        "This notebook provides a step-by-step walkthrough of applying LLMs, vector embeddings and BigQuery vector search for anomaly detection in semi-structured logs using familiar SQL.  Use and extend this notebook to experiment with embeddings and evaluate vector search accuracy, speed and cost using the publicly available HDFS logs dataset or your own dataset.\n",
        "\n",
        "The performance achieved with HDFS logs dataset is satisfactory and incurs much lower overhead compared to other alternatives given the use of off-the-shelf embedding and Gemini models. More exploratory work can be done to further evaluate and optimize recall, from fine-tuning the embedding model itself to optimizing the vector search-based anomaly detection logic (e.g. distance normalization, dynamic vs fixed threshold, etc.). That also includes index-time settings (e.g. `num_lists` for vector index) and search-time settings (e.g. `normalization_type`, `fraction_lists_to_search` for vector search). Results will vary depending on your dataset and your specific configurations."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2a4e033321ad"
      },
      "source": [
        "## Cleaning up"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "21f2a7432655"
      },
      "outputs": [],
      "source": [
        "# Delete BigQuery dataset. Uncomment and run the command below if you want to delete the BigQuery set.\n",
        "# from google.cloud import bigquery\n",
        "# Do this only if the dataset is created for this demo.\n",
        "# dataset = f\"{PROJECT_ID}.{DATASET_ID}\"\n",
        "# dataset_object = bigquery.Dataset(dataset)\n",
        "# client.delete_dataset(dataset_object, delete_contents=True, not_found_ok=True)"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [
        "rYn2yZ90cRG5"
      ],
      "name": "bq-vector-search-outlier-detection-infra-logs.ipynb",
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}
